arXiv AI
Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos
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arXiv Machine Learning
Sep 25
Don't Read the Log: Execution Traces Contaminate Verifiers in Video-Generation Agents
The paper investigates how providing execution traces to multimodal judges in agentic video‑generation systems can bias their verdicts. On a benchmark of 109 two‑event clips, traces that falsely report successful tool calls cause large‑language‑model judges to incorrectly accept 78–90 % of failures, while contradictory traces lead to 100 % rejection of correct clips. The effect persists even when judges are instructed to consider only the video frames, indicating that the vulnerability stems from the judges’ learned trust in tool logs rather than the visual content itself.
arXiv AI
Oct 9
Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents
The paper introduces a method for co-evolving inspectable verifiers alongside self-improving agents. By synthesizing verifiers from clustered failures and selecting them based on agreement with a reference set, the authors demonstrate improved held‑out agreement on MBPP+ and outperform a bare LLM judge. They also show that removing anchor guards collapses the verifier into a vacuous grader, yet the collapsed verifier still trains skills effectively, indicating that downstream task scores cannot certify a self‑evolved verifier.
arXiv AI
Aug 26
ACE: A Self-Correcting Agentic Canvas Editor for Multi-Slide Presentation Automation
ACE is a self‑correcting agentic canvas editor that operates on a hierarchical scene‑graph rather than flat document formats, enabling reliable multi‑slide presentation automation. It pairs a presentation‑specialized action space of 98 tools with CARE, a content‑aware router that reduces input tokens by about 89%, and a ground‑truth‑free instruction‑following judge that feeds natural‑language critiques back into the agent for self‑correction. In benchmarks, ACE outperforms a comparable agentic HTML pipeline on instruction following (4.23 vs. 3.81), runs 1.75× faster, costs 44% less, and is preferred by 58.7% of blind raters, with 81% favoring the self‑corrected output.
arXiv AI
Oct 9
Tracing the Thoughts of a Coding Agent Playing ARC-AGI-3: Lessons for Continual Learning
The paper investigates how a coding agent learns across a sequence of abstract reasoning tasks in the ARC-AGI-3 benchmark. The agent, operating on a frozen foundation model, writes and runs Python and shell scripts, retaining only its written artifacts as state. The authors develop a measurement protocol that traces each thought—any belief, rule, or plan committed to a file—through its lifecycle, revealing that most task-specific scripts are discarded at task boundaries and that the agent selectively forgets by rewriting knowledge into new scripts while accumulating unedited notes.
Hugging Face Trending Papers
6d ago
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments
The paper introduces Randomized Block Curation (RBC), a method for selecting useful lessons from language‑model agents’ persistent textual memory. RBC evaluates the impact of grouped lessons through randomized experiments on practice tasks, retaining only those that demonstrably improve task success. Experiments across two memory systems, three models, and three benchmarks show RBC raises average success to 66.9, outperforming both full playbooks and agent‑chosen lessons.
arXiv AI
Oct 5
Open-Endedness Bench: Measuring Epistemic Process from Agent Records
arXiv:2610.02588v1 Announce Type: new Abstract: Agents are increasingly given open-ended research tasks: discovering an empirical law from self-designed experiments, improving a heuristic whose optim...