THE_ROOM is a local-first Electron + React desktop app for running codebase-scoped multi-model collaboration rooms. It is built for technical operators who need persistent rooms, breakouts, durable artifacts, and guest participation without repo mutation privileges.

OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.

This week, Google Finance launches its AI-powered version across Europe with full local language support, introducing features like AI research, advanced charts, real-time intel, and live earnings calls.

The article explores why intelligent individuals are susceptible to AI hype, analyzing the erosion of cognitive defenses, local bias, and information bubbles. It argues that heavy AI usage induces delusions and a warped worldview, exemplified by cybersecurity experts' irrational fear of Claude Mythos and GlassWing. The author calls for maintaining two thoughts simultaneously: AI is useful yet overhyped.

Hopper is the first agentic development environment tailored for mainframes and COBOL, combining a TN3270 terminal, mainframe-aware panels, and an AI agent to expedite debugging of mainframe workflows.

Fields Medalist Timothy Gowers used ChatGPT 5.5 Pro to solve an open problem in additive number theory in just 17 minutes, producing a result worthy of a PhD thesis. Gowers warns this raises the bar for PhD students, while Terence Tao emphasizes the irreplaceable human role in 'digesting' proofs.

This paper proves that aerodynamic co-contraction in a redundant dual-rotor actuator can tune passive aero-mechanical damping while keeping net force constant. It defines an incremental damping coefficient that increases monotonically along constant-force fibers under aerodynamic hardening. Using Blade Element Theory, the authors derive a minimal thrust model and formalize the Variable Aerodynamic Damping Actuator (VADA), showing its dynamic isomorphism with variable stiffness actuators. The principle also enhances active aerodynamic promptness in multirotors, and an impedance-form representation clarifies roles of common-mode and differential-mode actuation.

This paper proposes a lightweight autonomous exploration system using omnidirectional vision and sparse topological maps for micro UAVs. It employs multi-fisheye cameras for full FoV and depth estimation, represents frontiers as topological nodes, and achieves efficient exploration with low memory and computation. Validated on a palm-sized UAV (11cm wheelbase, 400g weight).

PISTO is a novel stochastic trajectory optimization algorithm that stabilizes updates via KL regularization, achieving 89% success rate in robot motion planning, outperforming CHOMP and STOMP while generating shorter, smoother paths.

This paper introduces Dr-BA, the first radar bundle adjustment framework that operates directly on 2D spinning radar intensity images. Radar is robust to precipitation, making it critical for all-weather autonomy. Dr-BA uses full radar returns from multiple scans to jointly estimate dense maps and sensor poses via separable optimization, decoupling pose estimation from mapping, and naturally extends to direct radar-only localization (DRL). Achieves state-of-the-art performance on over 200 km of on-road data across five routes.

Proposes Intention Assimilation Control (IAC), which estimates the leader's target position instead of current position, enabling accurate tracking at low stiffness for safer teleoperation. Experiments on 7-DOF manipulators show IAC outperforms tele-impedance control in accuracy, task completion, and time.

An adaptive controller enables two UAVs to collaboratively transport bendable objects without explicit elasticity models, with asymptotic stability proven via Lyapunov analysis and validated through hardware experiments.

A new approach casts scenario orchestration as a constraint satisfaction problem, using foundation models to translate natural language into constraints and solvers to generate actor behaviors. It significantly outperforms baselines in orchestrating diverse test scenarios for autonomous vehicles.

This paper presents an aerial manipulator platform integrating RGBD perception, MPPI UAV control, and a lightweight 2DoF manipulator for precise flower detection and targeting in vertical farming. Experiments demonstrate centimeter-level end effector accuracy, laying groundwork for contactless pollination systems.

A new control framework uses screw-theoretic Lie algebra and partial differential equations to manage flexible multibody robotic manipulators. The approach decomposes the system into subsystems, achieving global exponential stability and modularity through Lyapunov-based methods and adaptive parameter estimation. Numerical simulations validate the scheme's physical consistency.

This paper introduces Heterogeneous Step Allocation (HSA), a training-free inference algorithm that assigns varying step budgets to different spatiotemporal tokens based on velocity dynamics. By introducing a KV-cache synchronization mechanism and a cached Euler update, HSA accelerates video generation without sacrificing global context. Evaluated on Wan-2 and LTX-2 models, HSA significantly outperforms prior methods at aggressive acceleration regimes, achieving a superior quality-runtime Pareto frontier.

A data-centric adaptation of Confident Learning for segmentation enables detection of label bias in training data without clean annotations. The method isolates directional errors and leverages bias-induced changes in feature space separability for mitigation, achieving equitable performance across three datasets.

TriDE is a new method for global camera pose estimation that leverages camera-triangle consistency to refine pairwise translation directions, improving accuracy and downstream camera location estimates.

AdpSplit is a new adaptive split operator that reduces the number of densification iterations in 3D Gaussian Splatting (3DGS) training by determining the number of split children from L1 pixel error region statistics and initializing child parameters, cutting training time by 9.2%-22.3% across datasets while preserving rendering quality. Combined with FastGS, it matches full-schedule PSNR on MipNeRF360 with 16.4% training time reduction, a 12.6x acceleration over vanilla 3DGS.

This paper investigates scaling laws for knowledge transfer in 3D medical imaging, revealing asymmetric transfer between modalities and proposing a data allocation optimization that improves performance by up to 58%.

LookWhen is a video recognition framework that uses a selector-extractor mechanism to learn when, where, and what to compute, approximating full-video representations without processing all tokens, achieving a better accuracy-computation trade-off. Experiments show it is 6.7x faster than InternVideo2-B at equal accuracy.

This paper proposes R$^3$L, a framework addressing error accumulation in multi-hop relative spatial reasoning for 3D layout generation. It introduces invariant spatial decomposition, consistent spatial imagination through an imagine-and-revise loop, and supportive spatial optimization via global-to-local coordinate re-parameterization. Experiments demonstrate physically feasible and semantically consistent layouts. Code is open-sourced.

HumanNet is a one-million-hour human-centric video corpus covering both first- and third-person perspectives, fine-grained activities, human-object interactions, tool use, and long-horizon behaviors in diverse real-world environments. It provides interaction-centric annotations including captions, motion descriptions, and hand/body signals. A systematic data curation paradigm transforms unstructured internet video into a scalable substrate for representation learning, activity understanding, motion generation, and human-to-robot transfer. Initial validation shows that continued training with 1,000 hours of egocentric video from HumanNet outperforms 100 hours of real robot data from Magic Cobot on a Qwen VLM model.

A comprehensive survey on edge deep learning for computer vision and medical diagnostics, covering principles, hardware classification, lightweight design, model compression, applications, and future challenges.

Researchers propose a measure transport framework for visual text compression (VTC) to predict information loss. They decompose the cost into precision and coverage terms, enabling a label-free routing criterion and adaptive foveation. On 24 NLP datasets with Qwen3-4B, their method matches the per-dataset oracle on 70.8% of datasets, improving task scores by 3.3% while reducing token usage by 10.3%.

The paper introduces GoSkills, an inference-time group-structured retrieval method that transforms flat skill lists into compact, role-labeled execution contexts (Start, Support, Check, Avoid), improving skill-augmented agents' efficiency. Experiments on SkillsBench and ALFWorld show gains over baselines.

The paper introduces MultiSoc-4D, a benchmark dataset of over 58,000 Bengali social media comments annotated for category, sentiment, hate speech, and sarcasm. The authors use multiple LLMs (ChatGPT, Gemini, Claude, Grok) to annotate the data and discover a phenomenon called 'instruction-induced label collapse,' where LLMs overuse fallback labels, leading to high agreement but low detection of minority classes. For instance, they detected only 21% of hate speech and 25% of sarcasm instances compared to human annotators. The work highlights a 'label agreement illusion' validated by near-zero Fleiss' Kappa, and tests over 40 LLMs to show the bias propagates regardless of architecture.

A new study evaluates large language models' ability to adapt responses to the certainty of retrieved information, finding systematic limitations and proposing an interaction strategy that reduces obedience errors by 25% on average.

This paper presents a framework for developing Human-Centered Large Language Models (HCLLMs), integrating NLP, HCI, and responsible AI perspectives, arguing that human concerns must be addressed at every stage of the pipeline, from design to deployment, and concludes with a case study on the future of work.

Researchers propose MIST, a synthetic multi-turn voice-driven code generation dataset for IoT devices. Experiments reveal a significant gap between open- and closed-weight multimodal LLMs, with even frontier closed models having substantial room for improvement. The dataset and extensible generation framework are released to facilitate research on mixed-initiative voice assistants that reason about physical world constraints.