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AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：IBM has rolled out the newest models in its family of open-weight large language models designed to be downloaded and self-hosted. The newly launched Granite 4.2 comes in 3B, 8B, and 30B parameter variants. Like previou…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Google has released Gemini 3.5 Transcribe, a speech-to-text model that ships as two separate endpoints rather than one. The streaming endpoint delivers sub-second transcription but drops speaker diarization and word timestamps. The batch endpoint keeps both, at half the cost. Google reports 4.0% word error rate streaming and 2.6% non-streaming, with 70% faster finalization than Chirp 3. Here is what the split means for anyone building voice agents or transcription pipelines. The post Google AI Releases Gemini 3.5 Transcribe: A Speech-to-Text Model Reporting 2.6% Average WER Across 85+ Languages appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26622v1 Announce Type: new Abstract: Achieving stable, sustained grasping with soft robotic hands remains a fundamental challenge. Compliance enables safe and adaptive contact, yet the intrinsic viscoelasticity of soft polymers leads to stress relaxation and a continuous decay of grasping force during holding. Inspired by human grasping, which combines phase-dependent stiffness regulation with continuous sensing and feedback, this paper presents an integrated structure--perception--learning framework. We develop a variable-stiffness soft gripper that uses onboard vision and infrared thermography to track deformation and the temperature field in real time, preserving continuous tracking of the interaction state. To mitigate relaxation-induced force decay, we propose a temperature-coupled viscoelastic force representation, together with a physics-informed learning model, to reconstruct the force trend and provide explicit compensation during holding. Experiments show that, in a 280s force-controlled grasp-and-hold task, the proposed method maintains the desired force with a mean absolute error of 0.066N, outperforming fixed-aperture and instantaneous-only baselines by 80% and 95%, respectively. Overall, the results support a mechanism--AI co-design view: mechanisms shape feasible interactions, while learning compensates remaining uncertainty in viscoelastic dynamics, together enabling stable, sustained grasping.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26578v1 Announce Type: new Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual triggers and induce configured failure modes. Unlike prior backdoor attacks that treat any task failure as a successful attack, Configured Failure Trapping requires the attacker to control how the robot fails (e.g., causing the robot to grasp with a specified positional offset), making it substantially more challenging and hard to detect. To support the new task, we propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity. Then, based on this foundation, we construct two new benchmarks, namely Trap-LIBERO and Trap-RoboTwin, that instantiate Configured Failure Trapping across four representative failure modes. To address this task, we identify sparse action deviation as a critical challenge and accordingly propose a novel method named TrapVLA, which explicitly learns trigger-induced action residuals to steer the policy toward the configured failure behavior. Extensive experiments across simulation benchmarks and real-world robotic settings show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data. Project page: https://john-liua.github.io/TrapVLA/

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26496v1 Announce Type: new Abstract: Navigation in unknown environments to find unforeseen objects has become increasingly feasible with capable vision and language foundation models. However, these models also introduce non-negligible inference latency, which becomes an important concern when agents must operate continuously in the real world. Most state-of-the-art methods are still developed in synchronous simulators, where the environment waits for the agent to act and inference time is effectively free. As a result, agents are often designed around the sequential execution of perception, reasoning, and action, with little regard for time constraints. Under real-time execution, where wall-clock time counts towards the task budget, the inefficiencies of these architectures become clear. We show that recent zero-shot object navigation methods suffer consistent performance degradation under such realistic timing conditions. Motivated by this observation, we propose RTNav, a simple but effective architecture that treats inference latency, asynchronous environment stepping, and bounded compute as explicit design considerations. Evaluated on real-time variants of HM3D-v1, HM3D-v2, and HM3D-OVON, RTNav improves the success rate by up to 11% and the Success weighted by Completion Time by up to 5.1 points over prior work.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26383v1 Announce Type: new Abstract: Autonomous robots performing laboratory tasks depend on 3D reconstruction pipelines that can turn raw camera streams into actionable object representations within the latency budget of a physical control loop. Neural 3D reconstruction methods have demonstrated high-quality view synthesis, but their real-time viability across the compute platforms on which laboratory robots actually run remains poorly characterized. In this work, we present a systematic compute-platform benchmark of neural 3D reconstruction methods, evaluating NeRF and 3D Gaussian Splatting training and rendering on GPU-enabled computing devices ranging from single-board computers to server-class nodes, and place Meta's SAM3D single-image reconstruction on the same axes to quantify its latency and fidelity gap relative to per-scene optimization. Our results show that Gaussian Splatting yields higher rendering quality than NeRF at greater GPU cost, and that onboard compute is insufficient for full per-scene optimization at interactive rates. Our preliminary assessment on SAM3D indicates that it delivers plausible object geometry within seconds, but with detail mismatches that can compromise downstream manipulation. Together, these findings motivate tiered pipelines in which lightweight feed-forward reconstruction sustains the real-time perception-and-tracking loop for laboratory robots, while heavier neural reconstruction is scheduled selectively on suitable compute.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26273v1 Announce Type: new Abstract: Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a closed chain that must satisfy both static equilibrium and geometric loop-closure constraints. This paper presents a constraint-aware physics-informed neural network (PINN) for static shape estimation of a tendon-driven CCR modeled using the geometric variable strain formulation. The proposed method incorporates a projected static equilibrium residual and a configuration-level geometric residual to enforce the governing mechanics and closed-chain geometry. In simulation, the PINN is compared with a purely data-driven artificial neural network (ANN) under limited and noisy training data. With 140 samples and 50% label noise, the PINN reduces the relative configuration error, equilibrium residual, and closed-chain residual by 67.88%, 67.35%, and 88.06%, respectively. Using the full dataset, the PINN achieves 0.1597% relative configuration error with an inference time of 0.1773 ms, compared with 17.97 s for an iterative nonlinear solver. Experimental fine-tuning reduces the marker RMSE from 2.657 mm to 0.497 mm and increases R2 from -0.788 to 0.937. These results demonstrate accurate, physically consistent, and computationally efficient static shape estimation of closed-chain CCRs.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26239v1 Announce Type: new Abstract: Generative world models provide robots with predictive models of how the world evolves under interaction, with growing potential for simulation, planning, policy evaluation, and robot learning. Beyond clip-level future prediction, a unified generative formulation should relate actions to consequences, support flexible horizons and continuous interaction, and enable reward-driven optimization. We introduce WALL-SS, a world model that generates visual futures through Scale-wise autoregressive Scaling, enabling action-controllable and long-horizon robotic simulation. WALL-SS represents embodied trajectories as causal sequences of temporally interleaved observations and actions, making action-dependent state transitions explicit while naturally supporting variable-length generation, streaming extension through reusable causal states, and direct optimization through sequence probabilities. To make this formulation effective over long horizons, we generate each future observation in a coarse-to-fine manner and develop three complementary components within the same hierarchy. Action-conditioned next-scale prediction injects scale-aligned action representations to improve action-future coupling and model both successful and failed behaviors. Scale-compressed long-horizon memory retains recent interactions at fine resolution while compressing distant observations and actions, with scale-wise dream forcing enhancing robustness to self-generated context. Finally, on-policy alignment optimizes autoregressive visual dynamics with action-following and long-term consistency rewards while preserving the pretrained visual distribution. Experiments show that WALL-SS improves action following and trajectory accuracy, supports coherent minute-long streaming rollout under bounded memory, and consistently benefits from on-policy alignment in reducing action drift and long-horizon inconsistency.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26495v1 Announce Type: new Abstract: Multimodal queries can require different types of reasoning. Some can be answered via perceptual reasoning, extracting information directly from the visual signal, while others require compositional reasoning that combines observations or deliberative reasoning that evaluates competing hypotheses. However, many existing methods apply a uniform reasoning strategy across queries, leading to unnecessary computation on simple tasks and insufficient reasoning on complex ones. We introduce Video-FLAIR, a training framework that learns to select the appropriate reasoning mode for each query using reinforcement learning. During training, the model generates responses under all three modes for the same prompt, enabling direct comparison. A composite reward compares these responses to favor the most effective one based on correctness, grounding, and cost, while discouraging unsupported or misaligned deliberation. This yields a supervision signal for learning adaptive reasoning without per-query annotations. Video-FLAIR improves accuracy over the Qwen2.5-VL base model by +5.4 on MathVista, +4.8 on Video-Holmes, and +4.8 on Video-MMMU, while reducing average token usage to 95 compared to 417 for always-thinking baselines.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26476v1 Announce Type: new Abstract: Zero-shot image restoration methods with text-to-image latent diffusion models have achieved great success in universal image restoration tasks without training. However, applying them to video restoration will result in severe temporal flickering. In this paper, we propose a novel framework for zero-shot video restoration and enhancement which uses a text-to-image latent diffusion model and multi-modal references. Through the proposed dual prompt tuning inversion and sampling, the inference time can be reduced to nearly 1/3 of the original. The performance and temporal consistency can be also significantly stregthened. By using the proposed texture-aware video token merging, the temporal correlation between frames can be further utilized to improve the temporal consistency. We futher propose the referenced self-attention and referenced token merging to support image reference. Experimental results demonstrate the superiority of the proposed method in restoring and enhancing temporally consistent videos.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26382v1 Announce Type: new Abstract: Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human pathology largely unaddressed. This gap is especially important in toxicologic pathology, where microscopic tissue examination of laboratory animals is a core component of preclinical drug safety assessment. To address it, we introduce VIPER, the first expert-curated benchmark for vision-language model evaluation in toxicologic pathology. VIPER contains 1,251 questions associated with 419 H&E-stained rat histology images across seven organ systems, covering multiple-choice, KPrim, and free-text formats. All questions were curated and validated by board-certified veterinary pathologists. In total, we benchmarked 16 models, including two newly introduced veterinary-pathology models, seven human pathology-specialized models, and seven general-purpose frontier models. The results identify a substantial domain gap between veterinary and human pathology, expose the risk of over-diagnosis of normal tissue in frontier models, and show that domain-specific training remains critical for visually grounded predictions. VIPER data and evaluation code are available at https://github.com/mahmoodlab/viper.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26363v1 Announce Type: new Abstract: This paper introduces a unified mathematical framework for modeling information propagation through convolutional neural networks (CNNs), with the aim of connecting descriptions of physical space and information space. A correspondence is presented linking discrete filter symmetry and the relativistic energy--momentum relation under the widely used nonlinear rectified convolution operation. Specifically, symmetric filter components (e.g. the sum $\Sigma = [1,1]$) operate analogously to rest energy $mc^2$ in preserving the image centre of mass (e.g. isotropic diffusion), whereas antisymmetric components (e.g. the gradient $\nabla = [-1,1]$) operate analogously to the momentum term $pc$ in generally inducing a displacement (e.g. vibration or translation). For typical small discrete filters, this displacement is determined by the ratio of antisymmetric to total filter energy, analogously to how the displacement of a relativistic particle relates to a Lorentz transform with beta parameter $\beta = \frac{v}{c}=\frac{pc}{E}$ equal to the ratio of momentum $pc$ to total energy $E$. Repeated filtering leads to the Gaussian scale-space and emergent scale-invariant features. These constructions share a Laplacian-driven structure with the classical heat (diffusion) equation and, via standard mathematical correspondences, with the Schr\"odinger equation and aspects of the Friedmann equations, together with emergent Morse topological structure. Demonstrations in 3D images reveal blob-like, scale-invariant Morse critical points in images spanning a wide range of physical scales, including organic sugar molecules and inorganic silicon crystals, human and primate brains in magnetic resonance images (MRI), galaxies and the cosmic microwave background (CMB).

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26355v1 Announce Type: new Abstract: While LVLMs rapidly improve, long-video question answering still remains challenging: relevant evidence is sparse, and question-relevant context often fails to provide cues that discriminate the correct answer from plausible alternatives. Diagnostic analysis on a manually annotated subset of MMR-V shows that prior agentic systems substantially improve cue retrieval over direct VLM inference yet fail to achieve a corresponding gain in answer accuracy, indicating that the bottleneck lies in option-discriminative evidence rather than topical relevance alone. We propose PACE (Progressive Acquisition of Critical Evidence), a factor-guided framework for long-video evidence acquisition. PACE proceeds in two stages: it first indexes clip-level descriptions guided by question-derived factors without observing the candidate answers; it then uses the candidate answers to derive contrastive cues and queries the index for verification. On MMR-V with the open-source Qwen3-VL backbone, PACE achieves 42.6% accuracy, outperforming direct inference and prior agentic baselines including Deep Video Discovery (DVD). On the same diagnostic subset, PACE recovers 66.9% of the annotated cues, providing empirical evidence that its gains are associated with improved evidence recovery rather than stronger answer-side priors alone. Consistent gains over DVD on LVBench, Video-MME, EgoSchema, and LongVideoBench suggest that option-aware evidence acquisition transfers beyond MMR-V. Code is available at https://github.com/HKUST-KnowComp/PACE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26317v1 Announce Type: new Abstract: Frontier language models are increasingly marketed as omni systems that can perceive and respond across modalities. Existing evaluation frameworks, however, focus almost exclusively on bimodal understanding, typically text plus one other modality. We propose the Modality Maturity Index (MMI), a benchmark designed to evaluate the multimodal capabilities of large language models across five modalities (text, image, audio, video and document) and combinations of up to three modalities in both inputs and outputs. MMI consists of 893 questions, each carefully crafted to require the model to demonstrate its understanding of multiple input modalities and to generate responses that incorporate various output formats. The questions are designed to be self-contained, with clear expectations for the correct modality or mix of modalities required for an accurate response. Every MMI prompt carries human-authored rubric criteria for each output modality expected in the response; a model's MMI Value expresses the average of the per-modality scores for each prompt. Because low scores can reflect either failure to generate a modality (lack of presence) or failure to generate correct content, we introduce also a supplementary Modality Presence Score (MPS), a per-prompt F1 over the expected output modalities. Applying MMI to five frontier multimodal models, we find that the MPS ranges from only 15.6 (Claude Opus 4.6) to 34.9 (GPT-5.4). Given the low availability of returned modalities to even grade, we report MPS as our main result pending model improvements. To assess the viability of judging output correctness with LLM judges and rubrics, we run a separate experiment with custom generation tools. On the assets that generates, we find that an LLM judge applying the rubrics agrees with rubric-blind human annotators (who score the outputs directly and never see the criteria) on 70.8% of judgments.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26238v1 Announce Type: new Abstract: Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp, it carries no part decomposition, and it exposes no parameter a user could edit. To address this, we explore the paradigm of 3D shape as code, leveraging and scaling the coding ability of an LLM for 3D modeling. We introduce Procedura, a novel 3D modeling agent framework that writes an object as a procedural assembly, a parametric program whose named parts are joined by typed, machine-checkable mates. From a text prompt, the agent plans the object as an assembly graph and writes the program part by part, solving each placement from the mated frames rather than guessing it, and admitting a part only once compile, mate, and connectivity checks pass. A decoupled vision critic then refines the assembly one diagnosed fix at a time. Moreover, the same graph carries per-part materials and a simulator-validated articulation. We evaluate on P3D-Bench under its assembly judge, and with the same judge on MechBench-36, our hard-surface benchmark. On both, Procedura outperforms state-of-the-art native 3D generators and every prior 3D-code agent on judged quality, produces the sharpest edges of any method we evaluate, and is the only one whose output is an editable, part-structured program.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26214v1 Announce Type: new Abstract: Surgical video data provides the primary training resource for models of intraoperative perception, surgical workflow understanding, and robotic decision-making. However, clinical data acquisition remains constrained by privacy, cost, and class imbalance. Surgical video generation has emerged as a transformative approach to addressing data scarcity and as a foundation for surgical simulation, training, and robotic policy learning. The field has developed rapidly without a clear conceptual framework. This survey organizes the 2024-2026 literature into three categories: unconditional generation, conditional generation, and world modeling generation, revealing a fundamental shift in how the task is defined from synthesizing visually plausible frames to modeling the causal dynamics of surgical scenes. We examine the persistent gap between pixel-level fidelity and clinical plausibility, and identify generalization, physical realism, controllability, and interpretability as bottlenecks. We further summarize experimental results of representative methods on public datasets to provide a quantitative reference for the field. This survey provides a structured overview of the current state and open challenges, offering a reference for researchers working at the intersection of intelligent perception, multi-modal fusion, generative AI, and surgical data science.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26126v1 Announce Type: new Abstract: Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specifications, operational telemetry, vendor-specific fault evidence, and exact RF/network calculations. However, current LLM integration in telecom remains bottlenecked by a two-sided capability gap: generic reasoners often lack telecom-specific grounding, while domain-specific telecom LLMs remain limited in structured, multi-step reasoning. To bridge this gap, we release TelecomGPT-R1-9B, a unified open-source telecom reasoner that ranks top-performing on the GSMA open telco leaderboard. Specifically, we curate a 67,427-example supervised fine-tuning (SFT) corpus organized around four complementary reasoning axes: protocol, knowledge, modeling, and fault. The corpus is built from axis-matched public web sources and enhanced through axis-specific chain-of-thought (CoT) generation and prefix-continuation self-validation. Starting from Qwen3.5-9B, we further develop a two-stage post-training recipe. First, multi-teacher low-rank adaptation (LoRA)-based SFT injects telecom knowledge and induces axis-specific reasoning formats. Second, group relative policy optimization (GRPO), stabilized by decoupled clip and dynamic sampling policy optimization (DAPO), optimizes the policy using four axis-aligned binary verifier rewards. Across seven public telecom benchmarks, TelecomGPT-R1-9B ranks first among open-source telecom LLMs and achieves a seven-axis mean comparable to state-of-the-art closed-source frontier reasoners.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26124v1 Announce Type: new Abstract: Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-ended. Traditional rule engines are brittle and costly to maintain, whereas unconstrained LLM agents lack the reliability and auditability required for financial decisions. We present a production-grade LLM-powered pricing system with a strict decision boundary: LLMs perform structured extraction and bounded policy/path selection, while all numeric pricing, including total-price computation, is executed deterministically. Policies are compiled into interpretable condition trees, enabling open-ended support for new clauses and evolving rules without code changes, while exposing auditable artifacts for human-in-the-loop control. Periodic fine-tuning on logged traces further improves tree induction and path matching. Deployed at a municipal state-owned tourism enterprise across 7 scenic sites and 12 business categories with 1,500+ operators and 1,000+ active policies, the system processed 3,960 orders in six months, reduced the order management team from 15-20 to 3, and cut per-order handling time from 10 minutes to <2 minutes.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26123v1 Announce Type: new Abstract: Large language models (LLMs) are trained predominantly on English-language internet text that over-represents certain cultural narratives, raising concerns that models flatten the diversity of non-Western storytelling traditions into a single homogenized archetype. We present a pilot computational study examining this across three maximally distinct Indian regional oral and literary traditions: the Rajasthani Pabuji epic, classical Tamil Sangam poetry, and Bengali folk tales. We collected authentic reference corpora for each tradition (11, 21, and 10 passages respectively) and prompted two LLMs (Claude Sonnet and Gemini) with 54 generation requests spanning three prompt types per tradition - generic, culturally specific, and regional-language. Using Sentence-BERT embeddings and cosine similarity, we measure reference drift (how closely outputs track their own tradition's authentic texts relative to the other two) and cross-tradition convergence (how similar outputs are across traditions). We find that while outputs remain closer to their own tradition's reference than to others, cross-tradition similarity is high (0.52-0.66) relative to what the traditions' genuine distance would predict, indicating partial homogenisation. Unexpectedly, prompting in the regional language (Hindi, Tamil, or Bengali) consistently reduced fidelity to the authentic tradition relative to English prompting, by as much as 27 percentage points for Rajasthani and Bengali traditions. We discuss this against conflicting prior results on multilingual prompting and argue it reflects a difference between eliciting general cultural diversity and simulating one narrow, lesser-documented oral tradition. We position this pilot as a lightweight, scalable complement to recent large-scale human-annotation studies of Indian cultural misrepresentation in LLM-generated stories, as part of a broader doctoral research program.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26121v1 Announce Type: new Abstract: Large language models state false facts as fluently as true ones, yet a model often "knows" internally when it is on shaky ground: the probability it assigns to its own answer tends to dip on the facts it gets wrong. The usual way to act on this, teaching a model to abstain rather than guess, requires a labelled dataset of right and wrong answers. We ask whether the model's own confidence, which is free and needs no labels, can do that job instead. We fine-tune each model (with LoRA) to answer when its frozen confidence is high and to say "I'm not sure" when it is low, using the signal alone and no correctness labels. Across six open-weights models (1B-8B, two families) on short-form factual question answering, with correctness adjudicated by an independent judge model, this label-free recipe holds its own against label-supervised abstention-tuning: at matched coverage we find no statistically detectable difference between the two. A control that drills hard examples instead of abstaining does not help, indicating the gain comes from calibration, not rote memorization. The signal's one blind spot is confidently wrong facts, which it cannot flag. A model's own doubt is thus a near-free substitute for a labelled dataset when teaching it when to abstain. Code and artifacts are available on request.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26120v1 Announce Type: new Abstract: Large Language Models (LLMs) are probabilistic models, typically defined by an autoregressive factorization. While recent work has begun to study richer target distributions beyond the base model, the sampling strategies remain highly inefficient. In this paper, we present a flexible and theoretically grounded framework for steering and scaling autoregressive LLMs with sampling. Within this framework, we describe two algorithms -- one based on Sequential Monte Carlo (SMC) and one based on Replica Exchange (RE) -- that steer generation toward powering, product or tilting of the base model distribution. We illustrate this framework through scaling the generation quality of LLMs without external supervision or reward models. Experimental results demonstrate our methods scale more favorably than Best-of-N and standard MCMC baselines. Overall, this paper offers a systematic recipe for probabilistic inference with LLMs via sampling.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26119v1 Announce Type: new Abstract: Whether large language models can be prompted to generate rhetorical fallacies on demand, and whether current safety post-training constrains this behavior, has received less attention than the related question of detecting fallacies in existing text. We close this gap with DeflectBench, evaluating 23,990 generations from four frontier models across three deflection strategies (whataboutism, ad hominem, red herring), seven prompt framings, and 80 claims spanning four controversy levels. Refusal is governed primarily by request structure rather than claim content. Per claim refusal varies by only 11 percentage points across the 80 claims, while a single prompt frame change can swing within model refusal by nearly 100 percentage points and switching the requested fallacy type can swing it by over 80 percentage points within explicit framings. An educational debate coach prompt framing collapses refusal to near zero across all four model families, but the bypassed behavior is not clean compliance. Models typically produce labeled compliance, naming the requested manipulation in the same response that contains it. The four models distribute differently across refusal, labeled compliance, soft refusal, and clean compliance. The code and dataset are released at https://github.com/ArtKanke/DeflectBench.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26112v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through a draft-then-verify paradigm. Building on this, tree-structured methods improve inference by organizing proposals into multiple candidate paths, increasing the accepted length. However, existing tree-structured methods use a single drafter for all drafting steps, creating a dilemma: a smaller drafter is fast but yields lower-quality trees, whereas a larger drafter improves tree quality but suffers from high latency. To address this, we propose TreeGraft, a multi-drafter framework in which drafters of different costs jointly construct a shared draft tree. TreeGraft uses the stronger drafter to rescore candidates by updating scores assigned by the weaker drafter, reselect grafting positions, and recover promising paths left unexplored. It also integrates stronger drafter expansions non-destructively, preserving existing branches that may still be accepted by the target model. Together, these designs improve the quality of the shared draft tree. To control the drafting cost, TreeGraft introduces a lightweight scheduler distilled from an offline value system to decide when to call the stronger drafter. Across 10 model pairs and 6 benchmarks, TreeGraft outperforms the better of the two fixed single-drafter endpoint strategies by 15.1% on average, reaching a maximum gain of 26.6%. Our code is available at https://anonymous.4open.science/r/TreeGraft-E983.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26423v1 Announce Type: new Abstract: This paper proposes a framework for constructing a classifier as a safeguard layer, and for developing a complementary diagnostic that identifies which of the classifier's confident decisions can be trusted. This framework, the Latent Diagnostic Taxonomy, consists of (i) constructing a dimensionality-optimized classifier, in which the embedding dimensionality is empirically selected via cross-validated performance rather than fixed a priori, (ii) locating a relatively small set of latent support vectors (~ 29% of total training examples) representing influential prompts for identifying tokens that alter the classifier's predicted labels, and (iii) utilizing such tokens and their associated attack magnitudes for constructing a diagnostic taxonomy. This diagnostic taxonomy provides an end-to-end guideline for flagging prompts that require different treatments: rely Safely on the classifier's decision; flag Heuristic Bias and Heuristic Override cases; route Insufficient Context cases for further human/safety review. Applying the framework to a classifier trained on a public prompt injection dataset, we find that a substantial fraction of its confident decisions (~ 77%) are not robust to removing a single token, and that this brittleness separates into two distinct failure patterns: a confidence calibration failure and a genuinely exploitable shortcut. For each zone of the taxonomy, we also recommend strategies for remediating diagnosed prompts. We illustrate the framework as a series of steps, demonstrating how each step operates.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26375v1 Announce Type: new Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined rules or constraints on its outputs. In many real-world applications, ranging from autonomous systems to network routing, such guarantees are essential. We propose CG4AI, a framework that builds a convex combination of AI models while enforcing linear constraints on the combined output. A master linear program (LP) determines the optimal mixture weights, while a pricing subproblem generates new models guided by LP dual variables, focusing attention on the most violated constraints. A cutting-plane procedure extends feasibility guarantees beyond the training set. We apply CG4AI to two problems: (i) digit classification on MNIST, where we demonstrate four distinct uses of constraints, learning from constraints alone, improving adversarial robustness, correcting misclassified examples, and enforcing output relabeling; and (ii) the multi-commodity flow problem, where link capacity constraints are enforced on neural-network routing predictors. Experiments on MNIST and standard SNDLIB benchmark networks show that CG4AI reliably produces feasible predictors while achieving better accuracy than single-model baselines.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26332v1 Announce Type: new Abstract: Managed LLM services are now part of real production systems, but model selection and service planning still rely heavily on capability benchmarks that reveal little about operational behavior after deployment. We present Operational Embedding (OpEmbed), a framework for learning compact operational fingerprints of LLM cloud services from structured, privacy-preserving support-case metadata, without using case text. OpEmbed aggregates model--time windows into an eight-channel operational signature and learns a low-dimensional representation via temporal contrastive learning, cross-view reconstruction, and generational-ordinality regularization. Evaluated on more than 33,000 production support cases spanning seven LLM families over 26 months at Google Cloud, OpEmbed recovers interpretable family- and version-level structure, improves leave-one-model-out operational forecasting over non-learned baselines, remains useful under limited early-window data, and supports cross-model fault-type transfer. We report the practical lessons learned from building and evaluating this tool for model onboarding, support readiness assessment, and operational monitoring.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26324v1 Announce Type: new Abstract: Best-of-N (BoN) sampling is the simplest and most widely deployed inference-time alignment strategy, but it suffers from two distinct problems: reward hacking, in which the selected response exploits errors in the proxy reward model, and the absence of any privacy protection for the sensitive human preference data used to train that reward model. We show that a single intervention-adding calibrated noise to reward scores before selection-resolves both. Our first result, Private Best-of-N (PrivBoN), establishes that Gumbel noise at an appropriate scale simultaneously provides $\epsilon$-differential privacy and implements KL-regularized alignment. Whenever the privacy budget exceeds a critical threshold $\epsilon^*$, the privacy-mandated noise is the regret-optimal regularization, and privacy imposes zero additional alignment cost-matching the information-theoretic skyline of Huang et al. (2025). Because $\epsilon^*$ depends on an unknown coverage coefficient, we introduce Private Inference-Time Pessimism (PrivITP), which combines $\chi^2$-regularized rejection sampling with a two-phase Gaussian mechanism. PrivITP achieves ex-post $(\epsilon,\delta)$-DP with a privacy cost independent of the number of responses $n$, cleanly decouples the regularization parameter from the privacy parameter, and attains the skyline up to a noise-inflation term. Experiments across several language models, datasets, and reward models confirm our results: PrivBoN and PrivITP are scaling-monotonic (unlike BoN, which degrades past a critical $n$), and PrivITP matches or outperforms PrivBoN at equivalent privacy levels, with the largest gains in the strong-privacy regime.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26288v1 Announce Type: new Abstract: Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum with a few Newton-Schulz iterations. Existing theory either replaces this iteration with the exact polar factor it approximates, or treats its finite depth as an approximation error, and thus the iteration Muon actually runs can only hurt the guarantees. We show that finite Newton-Schulz can instead be beneficial for nonsmooth nonconvex optimization. To this end, we analyze Muon through the online-to-nonconvex conversion, which views the update rule as an online learner and converts its regret bound into a stationarity guarantee. The finite Newton-Schulz iteration smooths the discontinuous polar map into a Lipschitz map of the singular values, and Muon with finite Newton-Schulz can be regarded as an online learner with a smoothed spectral potential. This smoothing is exactly what the conversion needs: we prove that a Newton-Schulz depth growing only logarithmically in the target accuracy suffices for convergence to stationary points in nonsmooth nonconvex optimization, whereas Muon with the exact-polar update may fail to converge. The resulting sample complexity bounds match the best-known guarantees for nonsmooth nonconvex optimization and are optimal for smooth nonconvex optimization up to problem-dependent factors. The argument extends beyond Newton-Schulz to general spectral maps with the same smoothing property.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26222v1 Announce Type: new Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, largely rely on response-level feedback: each candidate prompt typically requires generating a target-model response to evaluate its attack effectiveness. This process is expensive and, more importantly, provides only sparse guidance on strongly aligned models, where most candidates are rejected with the same failure outcome. This paper presents NeuronFuzz, a white-box fuzzing framework that exploits internal safety neurons as continuous execution feedback for LLM safety evaluation. A SafetyOracle converts safety-neuron activations into a continuous safety alarm score that serves as feedback for fuzzing and can be obtained during prefill, eliminating response generation from the fuzzing loop. To construct the SafetyOracle, NeuronFuzz uses template-invariant harmful and benign inputs and stability-aware selection to identify a compact set of safety neurons whose activations capture harmful-intent recognition. Moreover, since the safety alarm score is differentiable, NeuronFuzz uses its gradients to identify safety-sensitive template positions and a masked language model to generate fluent, context-compatible mutations while preserving original harmful payload and avoiding additional optimization variables. We evaluate NeuronFuzz across 21 text and multimodal models. Across five white-box source models, it achieves a 76-100% jailbreak discovery rate, outperforming baselines by up to 48 percentage points. Its optimized templates further transfer zero-shot to open-weight and six proprietary target models, achieving average ASR and top-5 ensemble ASR (EASR) of 69.6%/92.6% and 44.1%/60.0%, respectively.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26132v1 Announce Type: new Abstract: Labeled property graphs combine relational structure with heterogeneous textual and categorical properties attached to both nodes and relationships. Conventional graph neural networks typically represent these properties as static feature vectors, limiting their ability to determine which semantic evidence should influence message propagation for a particular prediction target. We propose SLM-Conditioned Hierarchical Relation Routing, an architecture that integrates a small language model directly into graph message selection. A topology GNN provides a stable structural representation and prediction anchor. For each target node, incident messages combine the neighbor's structural state, node-property encoding, relationship-property encoding, and relationship type. A parameter-efficient SLM processes structured graph soft tokens and produces a target-conditioned routing query. This query first selects relevant messages within each relationship type and subsequently routes information across relation-level summaries. The resulting representation provides a bounded residual update to the topology anchor, preserving structural evidence while allowing contextual semantic information to modify the prediction. The architecture supports interpretable analysis at both the neighbor and relationship-type levels and provides a general mechanism for integrating language-derived semantics into property-rich graph learning.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26150v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have created new opportunities to streamline and potentially automate many research processes, including systematic literature reviews (SLRs). This study reports an LLM pipeline development for extracting model-relevant information from 536 peer-reviewed agent-based modeling papers. We compare the results with those of a human-conducted SLR. Our results show paper-level accuracies of approximately 77.95% for GPT-4.1 and 81.67% for GPT-5.0. Field-level accuracy ranges from 32.40% to 100.00%, with more complex or subjective fields performing less reliably. Importantly, we find that agreement between LLMs is a potential indicator of output quality: low agreement may signal hallucinations, whereas high agreement combined with low accuracy may point to noise or errors in the human dataset. Overall, our study provides practical insights into prompt development and highlights both the potential and limitations of using LLMs for full-scale SLRs in the modeling and simulation domain.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26149v1 Announce Type: new Abstract: Multi-table learning remains a major challenge in machine learning for healthcare and other complex information systems. Relational data combine several sources of complexity, including large data volume, high-dimensional variables, high-cardinality categorical features, complex inter-table dependencies, and repeated temporal observations. We introduce the Relational Hypergraph Transformer (RHT), a unified architecture that represents relational databases as hypergraphs, learns pentadimensional embeddings (PentE), and performs sparse relational attention with complexity proportional to the average relational degree rather than the square of the number of entities. We formally define the architecture, derive the complexity of its attention mechanism, and provide an open-source reference implementation. We evaluate RHT on the public Synthea synthetic electronic health record dataset using multi-label prediction of SNOMED CT condition codes per encounter, a task characterized by high categorical cardinality and long-tailed label distributions. Comparisons with tabular, relational, and temporal graph baselines show that RHT produces more semantically coherent embeddings while remaining computationally scalable. In this benchmark, the highest rare-code recall is achieved by XGBoost, whereas RHT attains the strongest embedding semantic coherence. We also report ablation studies quantifying the contribution of each architectural component. Clinical validation on MIMIC-IV is planned following PhysioNet credentialing. Source code and experimental protocols are provided in the accompanying repository.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26145v1 Announce Type: new Abstract: Our research focuses on evaluating literature reviews generated in short and long context settings of large language models (LLMs) to investigate the impact of context window on the quality of AI-generated literature reviews and the role of AI in supporting literature review writing. Twenty AI-generated literature reviews based on research sources from Semantic Scholar and Arxiv were evaluated by two researchers across 15 dimensions. Our findings reveal that AI-generated literature reviews require human oversight to meet academic publishing standards. As context windows increase, LLMs can incorporate broader information and maintain coherence across longer inputs, but they also exacerbate issues such as content repetition, omission of critical work, and a tendency towards descriptiveness over synthesis. Our work shows that AI-generated reviews can provide foundational overviews, but their output must be critically evaluated and refined by domain experts. Future research should consider integrating other LLMs and fine-tuned models in different domains with hybrid approaches that combine human expertise with AI capabilities to address the limitations identified in this study.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answering), a deterministic tool-grounded multi-agent LLM framework for financial question answering. CIFQA separates language understanding from numerical execution by assigning specialized agents to query interpretation, routing, parameter extraction, computation planning, and response generation, while deterministic Python-based tools perform financial calculations and rule application. We instantiate CIFQA for fixed deposit query answering and evaluate it on a curated benchmark of fixed deposit queries. CIFQA achieves 95.54% accuracy on calculation-intensive queries and 90.87% overall accuracy, substantially outperforming direct LLM baselines even when provided with complete formulas, rate cards, and benchmark instructions. Ablation studies show that deterministic components such as exact rate lookup, tenure computation, rolling-year adjustment, and premature-withdrawal logic are critical contributors to performance. Notably, a 17B open-source backbone operating within CIFQA outperforms substantially larger frontier models evaluated with the same financial information, demonstrating that architectural design is a more important determinant of numerical reliability than model scale. While evaluated on fixed deposit queries, CIFQA provides a generalizable framework for calculation-intensive financial reasoning tasks.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26113v1 Announce Type: new Abstract: We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PICasso couples a structured NL -> YAML -> GDS generation pipeline with PDK aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX-based photonic simulation. To systematically evaluate AI-driven photonic design, we introduce PIC-Set, a benchmark of 36 parameterized PIC design tasks spanning core photonic primitives and multi-component circuits. Using PIC-Set, we benchmark several state-of-the-art Large Language Models (LLMs) under a unified evaluation protocol, including new metrics such as structural and functional $Spec@k$, optimization efficiency, and robustness under perturbations. Across the benchmark, PICasso significantly improves end-to-end specification satisfaction compared to vanilla LLM generation. Structural $Spec@3$ reaches up to 92.7% and functional $Spec@3$ up to 52% on high-complexity circuits. In addition, PICasso consistently reduces circuit insertion loss, lowering the mean loss from 4.98 dB to 3.25 dB (1.74 dB improvement) through simulation-guided optimization. These results demonstrate that structured domain constraints, physical verification, and simulation feedback transform LLMs from brittle netlist generators into practical PIC design agents capable of producing manufacturable layouts with competitive runtimes relative to manual GUI-based workflows.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26111v1 Announce Type: new Abstract: Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensive labeled run-to-failure data, and model interpretability. Recent Large Models (LMs), built upon Transformer architectures and self-supervised pre-training, offer a transformative new paradigm to overcome these long-standing bottlenecks. This review provides the first comprehensive survey of LM applications in BPHM, systematically examining how these models address challenges in the field. We begin by elucidating the foundational technologies enabling LMs, including Transformer architectures, self-supervised learning, large-scale multimodal datasets, and PEFT techniques. We then categorize recent progress along four critical dimensions: mitigating data scarcity, enhancing generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation. Despite promising results, significant challenges remain across data accessibility, intelligence validation, trustworthiness, and deployment feasibility. To guide future research, we propose a roadmap focused on building collaborative data ecosystems, validating intelligence for industrial applications, enhancing trustworthiness with physics-informed designs, and enabling efficient on-device deployment. This review establishes a systematic approach to understand and advance LM-driven BPHM, providing researchers and practitioners with essential insights for developing next-generation battery management systems capable of safe, reliable, and autonomous operation throughout battery lifecycles.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26109v1 Announce Type: new Abstract: Machine-learning models can predict ICU mortality accurately, but feature-attribution methods alone rarely provide the clinical narrative needed for bedside use. Large language models (LLMs) may bridge this gap, and multi-step agentic pipelines are a plausible extension because they separate data interpretation, guideline checking, and final explanation. This revised feasibility study preserves the original standalone-versus-agentic comparison while making the main clinical findings more explicit. Using the retained local eICU Demo artifact set (2,353 ICU stays; 8.1\% mortality), XGBoost achieved an AUROC of 0.855 (95\% CI 0.796--0.906) and an AUPRC of 0.332 (95\% CI 0.217--0.494). On a stratified 38-case explanation subset, the standalone LLM produced 1 explanation with explicit outcome leakage, whereas the four-step agentic pipeline produced none. Among the 14 cases that overlapped with the SHAP review subset, the standalone LLM showed higher SHAP alignment (mean Jaccard 0.171 versus 0.077) and higher direction consistency (92.9\% versus 78.6\%), while the agentic pipeline showed higher guideline grounding (0.762 versus 0.143), higher value specificity (0.236 versus 0.143), and slightly higher plausibility (0.700 versus 0.671). Clinically, the results suggest that agentic decomposition may improve safety-relevant grounding and patient-specific detail, but it should be paired with attribution-based checks before use in high-stakes risk explanation.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26107v1 Announce Type: new Abstract: Predicting students' academic risk in online education is crucial for enabling timely interventions that can improve retention and learning outcomes. However, existing models often suffer from limited early detection capability and insufficient interpretability, leading to a "black-box" trust crisis that hinders their adoption in real-world pedagogical settings. To address these challenges, we propose EduRiskX, a neuro-symbolic framework that integrates a temporal Transformer-based predictor with F-Logic symbolic reasoning. The neural component models longitudinal student activity sequences using temporal attention, class-weighted loss, and dynamic weekly truncation. Acting as a data-driven expert system, an F-Logic rule base -- grounded in established educational theories (Engagement Theory and Student Integration Model) to mimic the diagnostic logic of human educators -- is constructed exclusively from the training data. The neural risk probability and the symbolic confidence score are then combined through a logistic regression-based fusion mechanism that learns the relative contribution of each signal. Experiments on the Open University Learning Analytics Dataset (OULAD) using a strict 80/10/10 student-level split show that EduRiskX achieves an accuracy of 0.900 and an F1-score of 0.894 at the end of the semester (Week 38), with an average early detection week of 9.32 and a detection rate of 94.30 percent. Compared with state-of-the-art time-series models (PatchTST, iTransformer) and common deep learning baselines (LSTM, CNN), EduRiskX yields improved recall and earlier risk identification under identical conditions. Beyond predictive performance, the F-Logic module provides structured rule-based explanations linking predictions to observable behavioral patterns and educational theories.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding August 2, 2026 12 minutes Case study. A real GSM8K prompt decoded three ways under the same timing setup: autoregressive, the dFlash drafter alo…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://embracethered.com/blog/posts/2026/breaking-claude-code-opus-5-and-automode/">Breaking Claude Code Opus 5 Auto Mode</a></strong></p> Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently <a href="https://simonwillison.net/2026/Aug/8/auto-mode/">made that the default</a> and have made bold claims about its effectiveness.</p> <p>Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports <code>base64</code> without noticing that this will import and execute a local <code>struct.py</code> file extracted from the archive.</p> <p>In a few cases auto mode directly prevented the agent from preventing harmful code from continuing to execute!</p> <blockquote> <p>In a few runs Claude tried to terminate the malware process once it noticed the compromise, but Auto Mode denied the cleanup command.</p> <p>Claude detects the compromise, but <strong>Auto Mode blocks its cleanup command</strong></p> <p>The safety mechanism itself can become part of the failure. The classifier allowed the creation of the malware process, but then it blocked the command intended to stop it!</p> </blockquote> <p>I agree with Johann's conclusion here: the only safe way to run agents if there's any risk of attracting the attention of an adversarial attack is with a sandbox:</p> <blockquote> <ul> <li>Run unattended coding agents in a container, VM or OS sandbox.</li> <li>Restrict network egress.</li> <li>Monitor your agents.</li> <li>Do not expose home directories, SSH keys, cloud credentials,… to the agent runtime. [...]</li> </ul> </blockquote> <p>Tags: <a href="https://simonwillison.net/tags/sandboxing">sandboxing</a>, <a href="https://simonwillison.net/tags/security">security</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/prompt-injection">prompt-injection</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a>, <a href="https://simonwillison.net/tags/johann-rehberger">johann-rehberger</a>, <a href="https://simonwillison.net/tags/claude-code">claude-code</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Coding agents craft arbitrary code so securing them is more complicated than red-teaming. We post trained a cyber-security small llm, changed how it reasons and supplemented our controls using program analysis technique…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Cohere has released Parse (parse-v5.0), a 2.3B-parameter vision language model that converts PDFs, slides and images into Markdown with HTML tables, bounding boxes and image descriptions. It runs at $1.50 per 1,000 pages through the API, or on dedicated Model Vault instances from $2,500 a month. Cohere reports a ParseBench score of 79.2, ahead of Mistral OCR 4, Azure Document Intelligence and Databricks AI Parse — but that figure averages three of the benchmark's five dimensions and drops charts and visual grounding entirely. The post Cohere Releases Parse 5 (parse-v5.0): A 2.3B Vision Language Model That Turns Enterprise Documents Into Markdown appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India with India geographic cross-Region inference. If you have local data processing requirements, you can now use these models at scale while Amazon Bedrock keeps inference requests and data within India.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India with India geographic cross-Region inference. If you have local data processing requirements, you can now use these models at scale while Amazon Bedrock keeps inference requests and data within India.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Research August 27, 2026 For long-horizon agents, model capability alone does not determine system capability. Infrastructure orchestration multiplies what models can do. INT21’s SwarmOS tested this hypothesis on the AR…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Take that, OpenAI! Anthropic! Chinese AI models have surpassed their U.S. counterparts in token consumption on OpenRouter. You might think U.S. AI companies dictate the AI economy. You&rsquo;d be wrong. According to dat…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Today on Decoder, I’m talking to Verge senior AI reporter Hayden Field about some pure Decoder bait: the seemingly-endless org chart changes at OpenAI, and how all of them seem to consolidate power under cofounder Greg Brockman, the company’s president. While Sam Altman is the CEO and still OpenAI’s most public face, Brockman has amassed enormous power and influence within the top ranks of the company as other senior leaders have left in rapid succession these past few months. Verge subscribers, don’t forget you get exclusive access to ad-free Decoder wherever you get your podcasts. Head here. Not a subscriber? You can sign up here. Brockman now oversees the company’s entire consumer and enterprise product teams, including ChatGPT, Codex, and its major infrastructure build out. As Hayden recently reported, he is effectively now the day-to-day operational leader of OpenAI. This will have major effects on OpenAI as a company and its product strategy, at a time when it continues to cede ground to Anthropic in the enterprise and is preparing for a historic IPO. This is all against the backdrop of needing to turn a profit in the next few years and the company’s huge ambitions to replace both Google Search and the iPhone in the consumer market. So I wanted Hayden to break down for me what’s going on at OpenAI, and the increasingly important role Greg Brockman will play in its future. Okay: Verge senior AI reporter Hayden Field on Greg Brockman’s consolidation of power at OpenAI. Here we go. This interview has been lightly edited for length and clarity. Hayden Field, you’re The Verge‘s senior AI reporter. Welcome back to Decoder. Thanks. It’s great to be here. Always, always chaos when you’re here, Hayden. Absolutely. There’s never a calm week. If there’s a calm week coming up, I know something even crazier is coming the next week. We should just rename the show The Real Housewives of AI. [Laughs] Honestly, that would be fitting. Straight up, that’s what we should do. A lot of personalities, a lot of feelings, a lot of relationships that people have really come to value over a long period of time. And a lot of lore. A lot of lore that goes between all these people for years and years and years. It’s crazy. And now they’re even putting out profiles on some of their spouses. The circles and the people themselves are really interesting. Today the drama is about OpenAI and specifically Greg Brockman, who seems to be consolidating even more power at OpenAI. You just wrote a long story about this. It seems very clear that Greg is emerging as the central decision maker at OpenAI. Describe what’s going on. Greg has obviously been pretty influential at OpenAI for a really long time, but what’s different now is that he has so much control over the day-to-day operations in a way he didn’t before. He was always a cofounder. He was always heavily involved. It was him, Ilya Sutskever, and Sam Altman for a long time, all in these email threads that came out during the Musk v. Altman trial. We saw them all talking about the future of OpenAI, what it should look like, and all their strange dynamics with Elon Musk. But Brockman was in a big-picture role before, and now he’s increasingly taking on so much power in the day-to-day. Altman has been focusing more on the big picture, the IPO stuff, and the direction of the company at large. Meanwhile, Brockman is amassing all this power day-to-day as other executives leave. He’s been in control now of Codex, of its enterprise business, the consumer side. There are four arms of the company right now and he’s in control of all of them. It’s been really interesting to see how that’s happened over the last few months. Greg is a character in the AI story. Certainly inside of OpenAI, he’s been a central figure in a lot of the company’s drama over time. So if you’ve been paying attention to the AI industry, you’re familiar with him. But if someone haven’t, explain who Greg is and why he’s such a notable figure both in AI and at OpenAI. He has been on my radar for seven years now. He’s been on other people’s for even longer. He dropped out of MIT to join Stripe way back in the day. He was Stripe’s CTO during its really early explosive growth phase. He left in 2015 to cofound OpenAI. That’s how he came on the AI radar. Ever since then, he’s been a really influential figure. He, Ilya Sutskever, and Sam Altman were all heavily involved in the drama with Elon Musk really early on, raising money, trying to lure people over to OpenAI, hire top talent, and figure out who was going to control AGI in the event they ever made it. There’s a lot going on there. In the years following, Mira Murati joined and Brockman, Sutskever, Murati, and Altman became the four main players here. These were the most talked-about people at OpenAI. You saw them in the headlines the most. They had the most power at the company. And then when the board coup happened in 2023, that’s when Brockman flew onto the scene in a new way. He had been behind the scenes a little bit, under the radar. He was not really doing that many public interviews. I interviewed him in 2020, but it’s not like he was a talking head. You weren’t seeing that much of him unless you were really watching the company and the industry. But when Sam Altman got fired by the board, Brockman was so incensed that he quit immediately. He and Altman were going to start their own thing, or were potentially going to lead a department at Microsoft. That’s when he was making waves in a new way because he was hellbent on the drama. Brockman said, “Okay, if Sam goes, I go.” That’s when he became Sam’s right hand in a new way. Before that, he was just one of the many execs. They were close, but the board coup made them closer than ever. That’s also what made Sam know that he could trust him in a new way and thereby later greenlight him getting more power. This was a big bet in the politics of the organization. Mira was going to become the new CEO for five minutes. Then there was going to be another new CEO and it was unclear who would stay and who would go. Justifiably, Altman did not know who he could trust in that moment, except Greg Brockman was ride or die. He said, “I’m leaving. I’m going.” The five minutes that they were going to start a new division of Microsoft were some of the most hilarious five minutes in tech history. But Brockman had basically made it clear he was ride or die for Sam. Over time, that bet has paid off. That move has paid off. Even during that board coup moment, it was not clear that Sam would ever come back. I remember I was backpacking in Patagonia at the time and I didn’t have a laptop, but I wrote six articles on my phone. It was a crazy time. It seemed like Brockman and Altman were going to be fine, but just be on their own. Of course, all the hundreds of employees signing a letter that they would leave if Sam was not reinstated is what got the ball rolling on him coming back and then pulling an UNO reverse on all the board members who had voted to oust him, except one. That was a big moment where he didn’t know who he could trust. Some of the people who he had trusted the most were the ones who voted for him to be fired. That went a long way with him that Brockman quit as well. Ever since then, we’ve seen Brockman positioned in the public eye a little bit more. He’s been in the press more. This summer is when he really amassed a lot more power after a couple other executives left, and he’s now in control of quite a lot of pillars of the company. What’s really striking is, yes, we can talk about 2023 and the board coup and Sam going and coming back and who was going to stay or go then, but actually this year has been more dramatic in terms of executive departures from OpenAI. Just since April, the list is staggering. I’m just going to run down it: Bill Peebles who ran Sora; Kevin Weil, who was head of product at Instagram and then VP of OpenAI’s science arm; Fidji Simo, who was supposed to be head of product and was at one point the AGI chief, left on health leave and then just never came back; Kate Rouch, the Chief Marketing Officer; Srinivas Narayanan, the CTO of B2B; Brad Lightcap, who was the former CEO and the head of special projects; and then just recently Denise Dresser, the Chief Revenue Officer, who is an important character if your company’s about to IPO. That is a lot of people, and it seems like Brockman just took all of their roles as all of those people left. Is that what’s happening here? More or less. Some of them definitely affected his position less than others. For some of them, like CMO Kate Rouch, leaving didn’t really give him that much more power. But what was interesting is Simo, the ex-chief of AI at the company, is the one whose absence left a huge gap for Brockman to fill. He took over a ton of different aspects. As you just alluded to, a lot of the people who had just left the company had also recently changed roles. Through one way or another, a lot of these people leaving created a power vacuum that Brockman could then capitalize on. It’s not like he had this master plan of amassing power, but either way, he ended up with a lot of power. What is interesting is when Brad Lightcap transitioned roles, for example, one of these other people took over his COO responsibilities. Guess who it was? Dresser. Then she leaves. Now some of those responsibilities fall to Brockman. It didn’t all happen cut and dry, but in one way or another, people switching roles and then leaving the company, he ended up with a ton more power. I can’t even calculate the ratio really of how much power he ended up with. Now he’s in control — to give you some context — of the entire product strategy of the company, which is obviously incredibly important when you’re about to IPO and you’re getting a lot of pressure to turn a profit. He’s in charge of the company’s entire scaling arm, as well as core product and platform; all of consumer, which includes health, commerce, personal finance, ChatGPT, and other consumer-facing stuff; and core infrastructure, ads, data science, and growth. What isn’t he in charge of, really? You know what I mean? I want to talk about Fidji Simo for one more second here. She was the former CEO of Instacart, but before that she was a really high-ranking executive at Meta. She ran the Facebook app. She was in charge of a lot of monetization. When she came to OpenAI, it felt like her role was to turn the consumer version of ChatGPT into a product. There were lots and lots of people leaving Meta to go to OpenAI for a while, such that basically we had reported that every all hands at Meta was about, “What are you going to do about OpenAI? They’re taking all of our people.” You just saw this exodus of people from Meta going to OpenAI in various ways, led by Fidji Simo, in a moment where it felt like what they wanted to do was take on Google as a big consumer product supported by ads. It’s funny, Altman actually copped to that ambition on the David Senra’s show last week — here he is, giving Peter Thiel credit for saying OpenAI should compete directly with Google: Sam Altman: He’s like, ‘The power of this is the power of the Google text box, it’s a text box you can type anything into and it does the right thing. Clearly, the empty text box worked for Google so why don’t you just double down on that?’ That’s pretty straightforward and that’s what they did. Altman says they went super hard at it — being the empty text box, taking the Google business model, which is one of the best business models in internet history. That was a big bet, and they hired Fidji Simo to go run that bet. But she left, and that does not seem to be the emphasis anymore. The emphasis is on Codex, on enterprise, on competing with Anthropic. What’s left of that? Was that just a misfire and she had the wrong ideas? Is it still the plan? This seems like the question about OpenAI. Some of it has fallen by the wayside, which we’ve seen for a bunch of stuff that OpenAI has tried in the past year or two. They vowed to stop doing all their side quests, which is ironically something that Simo herself wrote in an internal memo. Several episodes ago, you were on and we just talked about the series of code reds at OpenAI. Right now they’re focused on the key revenue drivers, which are enterprise and coding. That’s really what they’re all-in on. They’re also, of course, really focused on building a “super app,“ which is supposed to incorporate all this stuff together, and making that super app better. Besides that, they’re trying to cut the fat and operate as a leaner business, which has been helped by some of these executive salaries being cut. They’re trying to look better on their balance sheet when they IPO. There’s a lot of pressure to do that, especially when SpaceX just IPOed at a crazy valuation and Anthropic is apparently gunning to exceed that. We’ll see if that actually happens. When they changed Simo’s title from CEO of applications to CEO of AGI deployment, that was the most Decoder thing of all. I looked at their org chart changing and I thought, “This isn’t going to last.” Over and over and over again, they keep reassigning people. And then as you say, they leave and we see one person take over those roles and consolidate power. We’ll come to the IPO in a second, but where is Sam Altman in all of this? It seems like he should be running OpenAI. He really likes focusing on the long-term stuff. The events of the last couple of years and all of the lawsuits have shown him that maybe he isn’t supposed to lead direct teams in a huge way. He has a few direct reports, but should he really be involved in the minutiae of the day-to-day operations? Maybe not. Altman has said before, and in blog posts that the company put out years ago, that this is the trend that they’ve been following. I remember a year or two years ago, the company would put out summer blog posts saying that different C-suite executives’ roles would be changing slightly, and Sam would be focusing a little bit on the longer term, on research. This is just a continuation of that trend, in my opinion. As we know — and this is a big Decoder thing as well — the more power you have at a company, the higher your title, the less you probably are involved in the actual day-to-day operations, especially at a tech company. This is another example of that. Sam has always been interested in the research and the long-term stuff. Brockman is an engineer. He was described really early on at the company as an “engineering workhorse that pushed to build scaled-up systems that would train the AI and make it work.” So he’s always been seen internally by employees as a can-do person. He makes things happen and he has the engineering training that is required to scale a system. So I could see investors being thrilled about this. I could also see Altman saying, “I know this guy’s in my corner.” Altman already cleaned out the board. He’s surrounded by people that are going to support him. He trusts Brockman. So he says, “I actually don’t really want to be involved in the operational stuff quite as much. I want to be focused on the direction of the company. So you handle that. You’re an extension of me. I can trust you. Go ahead.” That’s what I think is happening here. You’ve talked to Brockman before. I’ve met him. He just did a video with our friend Joanna Stern. You can see he’s pretty direct. He answers the questions. What kind of character do you think he’s going to be as a person who’s operationalizing OpenAI? Is this “get stuff done, make the numbers go up” situation? Or is this more expansive of an approach? It’s going to be interesting to see how he handles this much responsibility, especially this many disparate arms of the company. He’s a really can-do person. He has a lot of engineering training. He’s pretty respected within the company, but I do think there’s going to be a lot of natural tension here because consumer, enterprise, health, and personal finance are really, really intense departments that handle data extremely differently. And then you’ve got all the core infrastructure stuff and the behind-the-scenes stuff that makes everything work. We’re going to see a lot of natural tension arise, especially because this company has a limited amount of funds. Even if it’s a huge number, it’s still limited in some way, especially now compared to a few years ago. They have a limited amount of compute. That’s something that every executive at OpenAI has run into problems with — allocating the compute. The research side of things says they don’t get enough sometimes, or the product side says they need all of it. It’s going to be really interesting to see how he squares all this, especially as someone who’s probably trying to make everyone happy. He’s not going to be able to, and so he’s going to have to make some hard decisions. This brings us to the IPO, because the IPO is where those hard decisions have to pay off. There’s been a lot of talk about OpenAI playing catch-up to Anthropic, particularly in the enterprise and with coding. There was a Wall Street Journal story last week that said OpenAI’s revenue in Q2 lagged behind Anthropic’s. Obviously, Anthropic will tell you they’re profitable even though we haven’t actually seen their numbers and we don’t know how they calculate it. You’ve mentioned the IPO several times now. SpaceX just went public. Big IPOs are all the rage. What role is the IPO playing in all these changes? Is it focusing the company down? Is it making that equity pay off? Is it needing to raise more capital? What’s the shape of it? It’s absolutely behind a ton of these changes. It is normal to see a lot of changes in the C-suite ahead of an IPO. But some of the sources I spoke to within the industry, who study the way these IPOs typically happen in tech, said that the interesting and unusual thing here is how many C-suite execs left in such a short timeframe, because they know that looks bad for the company. It’s one thing if you need to cut salaries, make the balance sheet look a little bit different, make the company leaner. That’s normal. It is an easy way to do that, to not replace someone. We can only speculate how much each of these people’s salaries was. This is an easy way, cut and dry, to help out the balance sheet. But doing all of this in this amount of time reminds me of what we were talking about last time on Decoder about Google and DeepMind and how maybe Demis Hassabis didn’t leave when Jeff Dean did because they didn’t want to make the company look bad. Now at OpenAI, people are leaving left and right — sometimes within 24 or 48 hours. Brad Lightcap, for instance, just got a new job as special projects head. Then a couple months later he’s out, even after being at the company for so many years. Part of it’s probably just cashing out. You can make a lot of money if you’re an exec and the company’s about to IPO and you have a lot of stock options. But it is unusual, my sources said, that it’s happening in this short amount of time. The IPO is interesting for a number of reasons, but to me, the most clarifying is that OpenAI is up against Anthropic. There are numbers coming out, reported numbers, about Anthropic’s finances that make that company look pretty good. The company hasn’t really challenged them in public. So we have to assume that they’re happy that there’s reporting out there that the numbers look good for them. But all of that is enterprise. They’re selling Claude to big enterprises, to the government. It’s effective. Maybe their token prices have high margins. It’s unclear what the internals of Anthropic’s business look like, but the numbers that we can get look pretty good and they support a big IPO. There’s a lot of excitement around Anthropic for that reason. That’s Anthropic as an enterprise software provider. That’s their business. It’s a thing it’s focused on. It’s all they do. OpenAI has taken a lot of shots. You’re talking about cost-cutting ahead of an IPO. Are they shutting some things down? Are they closing the aperture on all the things they’re trying to do to compete with Anthropic ahead of this IPO? It’s interesting because I’ve seen a slight shift from OpenAI. Earlier this year, the company was saying, “Enterprise and coding. That’s what we’ve got to focus on. We have to make money. We have to compete with Anthropic.” Now, however, ahead of the IPO, OpenAI is getting a little bit of different advice. Yes, the company needs to focus on those things because obviously that’s where the money is. But now it’s also being told, “You also have to differentiate yourself from Anthropic. You can’t just be a copy that’s lagging behind. You have to be doing something different.” OpenAI is going to really lean into consumer and hardware as well. One attorney told me that for OpenAI to be successful in the public eye and in investors’ eyes, the company needs “hardware and consumer products to sell to consumers, and luckily, that’s something that Brockman has experience with.” It’s hard to do everything, but they’ve had to narrow a ton and then slightly widen again and say, “Consumer is what we’re really known for.” They’re saying, “We’re the Kleenex of AI right now because of how we’re known in the consumer world.” People say, “I’m going to ask ChatGPT,” or, “I ChatGPT’d it.” And they’re talking about just using AI in general. Maybe they weren’t actually using ChatGPT, but that’s the way they’re referring to things a lot of the time. So OpenAI knows they need to capitalize on that. That’s one way they can be different from Anthropic. Of course, Anthropic also offers that, but they’re much more known for enterprise and that is why they’re making so much money. But OpenAI is leaning into hardware. The company has Jony Ive. It has to not cut some of this stuff entirely because then it’s going to look like Mark Zuckerberg with Meta. It can’t go all-in on something and then just completely cut it, or they’re probably worried about being a laughingstock. You have to really cut the true fat, which they did with a lot of their side projects. But as far as consumer, hardware, enterprise, and coding, those are their main bets right now. We’re going to see them really double down on that heading into the IPO. Come on, Hayden. Aren’t we actually in the metaverse right now? Technically, you and I are in the Metaverse together right this second. It worked. Absolutely. It happened. Should have made an avatar. Technically we’re here in our bodies on the internet. Yep, that’s true. What argument can you have except that the metaverse definitely worked and Mark Zuckerberg was super right about all of it? The consumer business is really hard. The scale of the play you need to make the consumer business work is on the order of fully overtaking Google, which seems very challenging. In hardware, you have to replace the iPhone. If you do anything other than replace the iPhone, people still have their iPhones. And then you’re going to lose to Instagram every single time. Have they said anything about how they plan to do either one of these things? Because replacing Google with a Google-level monetization engine seems very hard. Replacing the iPhone — even if you have Jony Ive — such that people don’t still have their iPhones, seems very hard. Neither one of these things has been in any kind of focus for me at least. Like we talked about last time, I really am skeptical. Hardware is hard. I’ve seen way too many companies crash and burn when they try to make an AI hardware device. It’s going to look beautiful because Jony Ive is in charge. But as for how useful it’s really going to be, especially in the era of AI populism when there’s a huge backlash against using AI, I don’t know how excited people are going to be on a broad scale to have something visible pinned to them or in their ears or on their table where you can tell they are using AI. It’s going to be really exciting for a subset of people, but for the broader public, Meta’s glasses are still uncool, still being called pervert glasses. With AI hardware, you’ve got a steep hill to climb. Let’s talk about what happens next with OpenAI, because those are the challenges. What you have now is a new leader who is, as you said, running most of the company. Greg Brockman was just on CNBC last week. He basically defended the turnover. He said this: Greg Brockman: “I’d say fundamentally, we’re a very resilient organization… If you look over the years, there have been different eras where we have different sets of leaders in place. I’m constant, Sam is a constant. I think that we are stronger because of that resilience and diversity.” So this might be what Greg Brockman has to say: “All these people are gone, but I’m still here. Sam’s still here, and the company’s still the same.” Do you think it’s just the thing that he has to say? Or do you think there’s something more real about OpenAI as a company, where you have these two leaders who are clearly ride or die for each other and you can swap in and out all these other executives and the company will still have its own unique vision? He would like us to think the latter, but it’s more of the former. It’s never good when you have a bunch of people leaving at once. It’s never good when you’re constantly restructuring or reorganizing the company. It’s bad all the way down. Employees are probably feeling weird. They have a new person in charge. They’re being shuffled between different teams. Their teams are headed by one person and then their boss’s boss is someone else, which probably means their bosses are weird. It’s not great for productivity, especially heading into such an important time for OpenAI. Even if it’s a move that makes sense and is good long term, it’s still going to be weird for people in the short term. In some ways the latter is true in terms of Brockman and Altman having been at the helm from the start. Someone who’s been there for eight months who leaves is not going to have as much of an impact. But we’re seeing some people that have been there for a really long time leave as well, like Brad Lightcap. He was someone that I had tracked for years and years. All of a sudden he’s out, after switching roles, and now he says he’s going to start something new. He says he’s implying he’s still going to work with OpenAI in some way, shape or form. He and Altman had a really friendly exchange on X after he left, but a lot of it’s for show. They’ve got to make sure that people aren’t skittish about the company right now. It’s something he just has to say. But it’s also true that if there were two people that would be the most influential if they left, and luckily those two are still there, Brockman and Altman. You’ve mentioned the public antipathy towards AI several times now. There is just a lot of anti-AI sentiment out in the world. And it’s pretty politically coded, although it scrambled some political lines. Hating AI is pretty bipartisan. Hating data centers is pretty bipartisan. We’ve got a lot of reporting about that on the site. Brockman is into politics. He donated $25 million to MAGA Inc. He’s one of the top donors to Trump overall. Do you think him being so openly political will help or hurt him as he becomes more of a character, more of a visible leader of OpenAI? It’s going to help the company from the outside, because they’re trying to push a lot of stuff through during the Trump administration and they really want a voluntary regulation framework. They want to be in Trump’s ear in a good way. It’s going to hurt Brockman internally, because a lot of employees at tech companies that I’ve interviewed in the past year, no matter what company they work at, are incredibly angry if their CEO or their C-suite isn’t doing enough to speak out against some of the Trump administration’s decisions. Now, Brockman isn’t just not speaking out against them, he’s also supporting the admin with tens of millions of dollars. I could see this really hurting him from the inside. Maybe some people don’t take him seriously. Maybe we will see some departures. It’s going to help the company from the outside, just because when you’re giving a lot of money to Trump, he seems to let you curry favor with him. We’ll see. To be clear, we’ve seen Sam Altman stand next to Trump and announce data center projects. So whatever reputation OpenAI was going to have because of its leaders, it might already have, but the specific political giving seems new. And as we head towards the midterms, it seems like a new challenge for the leaders of the company to be openly associated with. Do you think it’ll change any of the valence around data centers and AI, how the public feels about them? I don’t know that it’ll change that because we’ve been careening towards this for a while. People are really mad about data centers and it’s a bipartisan feeling, like you mentioned, and same with AI. Sam Altman and other AI CEOs have said in the past month that they feel like AI has a big PR problem and that they haven’t done a good job of showing people the good parts of AI and how they need to do better to show people why they’re even building it in the first place. Maybe they do, but people have been told a lot of the good things about it and they’re still not thinking that they’re as good as the bad is bad. The AI industry is in for a rude awakening there. But as for what you mentioned about Altman, yes, he and every other AI CEO have been at Trump’s dining table. They’ve been caught on hot mics praising him. It’s not something hugely new that Brockman’s giving this money, but what is new is that he’s giving it so much of it in a personal capacity, so much so that Altman even had to come out and say at some point, “Brockman only did this in a personal capacity. We’re not saying that we’re super behind him.” He also couldn’t really say they weren’t behind him, but OpenAI had to separate themselves a little. That’s what’s interesting to me about this. It’s like the personal capacity of his giving has made so many headlines and OpenAI tried to distance itself a little bit, but not too much. Now they’re not going to be able to do that because he’s essentially running day-to-day operations. I’m very curious to see how that plays out. I don’t think we quite know yet, but as he becomes a more visible leader of the company, being that open about his politics, even in the context of the other big tech CEOs, is different. We don’t quite see that from all of them. We see it from Elon, but everyone else plays it pretty safe. This is new, especially for a leader at OpenAI. All right, let’s end here. Let’s say it’s two years from now, OpenAI is public. Do we think Greg Brockman is the CEO and Sam is just the “chairman of raising money,” or whatever it is that he’s doing? I could see that happening. It would take a couple of big things to make that happen. Altman really likes being in charge, so I don’t think he’s going to go super quietly. But if he ever needs to move on to a more big-picture chairman role, if the IPO doesn’t go so well or he gets dragged in the public eye or something and still needs to be part of the company but not the CEO, I could definitely see that happening. Especially because Brockman has a lot of ambitions. We saw his journal entries come out during discovery, during Musk v. Altman, and he was writing some pretty ambitious things. “What will get me to a billion dollars?” He’s saying, “This is my one chance to be in charge.” There are a lot of entries that he’s writing about how he’s put his blood, sweat, and tears into the company and how he needs to be in charge in some way, shape, or form. He will probably be gunning for a role like that and he’d be happy to take it on. We’ll see what happens in the next few years. But I definitely don’t think that’s out of the question, especially because he’s been there since the beginning. He has a lot of product experience and he’s clearly ambitious. The other thing that is interesting is, way back in the day, he wasn’t always so aligned with Altman all the time. In fact, Brockman and Sutskever, OpenAI’s chief scientist, were pretty aligned against Sam every once in a while. Not against him, but they were questioning him and pushing back. They were saying things like, “It seems like you really care about this CEO role. It seems like you really care about being in charge and having a lot of power in this way and that way. How does this relate to your political ambitions? How does this relate to who’s going to control AGI? We really don’t want it to be a dictatorship.” Altman would probably be hard-pressed to give up the CEO title when he fought so hard for it, even 10 or 11 years ago. But if he ever does, it seems like Brockman would be happy to take it on. I will end by stating my prediction that I’ve been making almost the whole year now. I don’t think we will end 2026 with OpenAI as the same kind of company as when the year started. Actually I would say, given all of this turnover, that prediction has already come true. Structurally, it is a very different company with very different goals than the year started. But I’ll just put that prediction to you. You cover the company way more closely than I do. Do you think there’s any way that OpenAI looks the same at the end of the year as it did at the top of the year? Absolutely not. When a company goes public, so many things change. Especially when the end of the year is only five months away. It’s a scary number of months away. It’s tomorrow. Let’s say maybe not by December 31st, but six months from now, 100 percent it’s going to look different and probably sooner than that. They’re going to have to make a lot of changes. They’re going to be listening to their investors in a new way and they’re going to be beholden to them in a new way. There are only certain parts of this industry that make money and OpenAI is not in the lead in those parts. They’re going to have to go all-in on that type of stuff. The type of research that they want to do to stay at the frontier, they can make a case for that. Money-wise, they can say, “Look, in order to stay in the lead, we have to do this long-term stuff a little bit.” But they’re not going to be able to go all-in on it all the time because it costs a lot of money — and they have a limited amount. We’re going to see a lot of the anxieties that we saw Brockman and Altman voice a year ago, two years ago. I’ve been in the room with them where they’re talking about their fear that they’re running out of compute and how they’re going to scale. Their whole job for the next six months, the next year, et cetera, is scaling. They’ve had so many concerns about this. They’ve had so many fears about how they’re going to do this with their limited compute, especially now that they’re going public and they’re beholden to investors in a new way. There’s no way the company is going to look the same. Hayden, this has been great. Thank you so much for being on Decoder again. We’ll have you back when another season of The Real Housewives of AI kicks off, which is probably going to be tomorrow at the rate we’re going. [Laughs] Absolutely. Thanks. Questions or comments? Hit us up at [email protected]. We really do read every email!

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