…Across two games (Cleanup and Gathering), two policy-synthesizer LLMs, and two welfare objectives ( utilitarian efficiency and Rawlsian maximin ), the researcher reliably exceeds hand-designed baselines, sharply tightens run-to-run variance…
…This framework provides (i) monotonic improvement on the listwise objective with bounded, zero-sum, and self-correcting projection gradients , and (ii) flexibility in divergence selection with distinct structural properties through the decoupled…
…While consistency methods enforce self-consistency along the full PF-ODE trajectory to steer it toward the clean data manifold, vanilla DMD relies on sparse supervision at a few predefined discrete timesteps…
…Co-Evolving World Models and Agent Policies for LLM Agents (2026) Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization (2026) Self-evolving LLM agents with in-distribution Optimization…
…overall, i like the clean geometry-prior split, and it would be interesting to see how this scales to multi-object scenes with occlusion. This is an automated message from the Librarian…
…construct a network that provably self-organizes to criticality. This is consistent with the self-organized-criticality / edge-of-chaos literature (Beggs–Plenz neuronal avalanches, J. Neurosci. 2003; Langton's "computation at…
…Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona…
…A Document Parsing Benchmark for AI Agents (2026) Self-Ensembling Vision-Language Models for Chart Data Extraction (2026) Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing (2026) ABot…
…Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search (2026) Towards Self-Improving Error Diagnosis in Multi-Agent Systems (2026) VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding…
…only 0.9% Runs with one fired error code have a 48% lower overall score than clean runs. ❓The problem: Most medical agent benchmarks score the final answer. But real medical-AI…
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