A GPU-native population optimizer, χ-sao (Convergence-Halt-Invert-Stick-And-Oscillate), exploits a deliberate convergence-anticonvergence oscillation cycle to escape local traps while freezing confirmed modes. On all 42 functions of the Simon Fraser University optimization benchmark suite across dimensions d ∈ {2,4,8,16,32,64}, χ-sao achieves 100% mode recovery where all CPU baselines collapse at d ≥ 8 on the hardest multimodal functions, with speedups up to 34× over basin-hopping on Michalewicz d=64 and up to 39× on Rotated Hyper-Ellipsoid d=64. Under substantial likelihood noise (σ_noise up to 1.0), mode detection remains 100% reliable. The algorithm is available as an open-source Python package on PyPI.

Researchers propose an attention-based, physics-guided convolutional neural network as a surrogate model to predict microstructural evolution in systems governed by the Cahn-Hilliard equation. The model accurately forecasts phase separation in binary mixtures over long times, preserves composition, and aligns with the Lifshitz-Slyozov domain-growth law.

A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is inverted: generating complex candidate solutions is no longer difficult, but reliably verifying them has become the harder problem. Every verifier is a proxy for human intent, never the intent itself, leading to a twofold difficulty: underspecified intent and optimization widening the proxy–intent gap. The paper characterizes verification signals along scalability, faithfulness, and robustness, and studies four reward constructions. Experiments show targeted verification design can suppress reward hacking and improve task completion quality, with the core observation that no fixed reward function remains effective as policy capability grows; verification must co…

COrigami is an end-to-end AI-driven pipeline that generates crease patterns from natural language, satisfying strict flat-foldability constraints and visual aesthetics. It assists human artists by generating structural starting points through steps including semantic stick figure generation, base packing, crease pattern solving, shaping, and reinforcement learning with an autonomous aesthetic evaluation loop.

This paper proposes a governance model for autonomous AI agents that does not monitor their reasoning but requires independently attested evidence at the point of high-risk actions. The agent retains autonomy over planning and reasoning, but execution of designated high-risk actions is conditional on preconditions attested by separate authoritative sources, cryptographically bound to a declared intent, and evaluated by a deterministic policy. Decisions are recorded in a tamper-evident log. A proof-of-concept implementation is presented with examples from software deployment and clinical prescribing.

Researchers propose DD-Elo, a skill assessment framework using drift diffusion model and move-level data, achieving faster adaptation than Elo while maintaining bounded deviation.

This study develops a provenance-aware, knowledge-graph-based multi-agent framework that integrates Reddit posts, WebMD reviews, and FDA adverse event records for nine antidepressants, achieving high entity recognition accuracy and revealing that patient-generated data provide partly independent safety signals, with community sources often preceding regulatory reports.

This paper introduces an LLM-powered comparative pipeline for large-scale governance discourse analysis of AI agent protocols. It validates the pipeline on two contrasting standards: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, it finds that while governance form influences substantive focus, both regimes exhibit comparable participation inequality and community fragmentation. However, discourse alignment is denser in the permissionless setting, suggesting open governance may foster greater thematic convergence.

AlgoEvolve is an LLM-driven evolutionary framework that generates, evaluates, and iteratively improves executable trading strategies. Across multiple experiments, the system exhibits emergent regime-adaptive strategy logic and introduces a meta-evolutionary outer loop to evolve prompts, improving search heuristics. The results demonstrate that LLM-based semantic evolution provides a viable approach for continual program synthesis in complex environments.

This paper shows that refusal in instruction-tuned chat models is gated by a compliant persona direction. Interventions on Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct demonstrate that steering compliant persona suppresses refusal (e.g., Llama's refusal rate drops from 97% to 2%), and refusal direction only partially restores refusal in late layers. The findings indicate refusal is expressed downstream of persona computation.

When a benchmark's accuracy saturates, it is often retired. This paper shows that this approach misses six other key dimensions: construct validity issues, out-of-distribution generalizability, efficiency, reliability, model versus scaffold importance, and human-agent collaboration uplift. Using CORE-Bench Hard, they surface construct validity threats, introduce an improved benchmark v1.1 and OOD suite, and find that the benchmark remains useful for measuring efficiency, reliability, and performance. A small-scale experiment shows human-agent collaboration yields about a 2x speedup.

Researchers propose a method to detect and control sycophancy in language models using cascading linear features. Their approach uses iterative data generation to isolate features that scale linearly with behavior, enabling better disentanglement. The discovered features form linearly separable subspaces, allowing for detection and steering away from sycophancy, outperforming baseline methods with lower computational cost.

Spin up a private, OpenAI-compatible LLM endpoint on Hugging Face infrastructure with a single command — no servers to provision, no Kubernetes, pay-per-second. Covers the full process from launch, querying, cleanup, scaling to larger models, creating a chat UI, SSH debugging, and using as a coding agent backend, with a comparison to Inference Endpoints.