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Notes on reasoning
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A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning
The study tests whether scoring signals in on-policy self-distillation localize errors and push probability away from erroneous student tokens. It reports that full-distribution KL and token log-ratio signals do…
GeoReform: Reflective Formalization Evolution for Multimodal Geometry Problem Solving
The preprint proposes GeoReform, which treats geometric formalization as an optimizable policy rather than a fixed parser output. It collects failed rollouts, diagnoses representation defects, and mutates how entities…
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness
The preprint tests whether legal models' named statutes or precedents causally support their verdicts. Holding case facts fixed, it substitutes the named authority and examines verdict changes and hidden-state dynamics…
ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement
The preprint describes an autonomous post-training harness that attributes errors, selects update directions, and executes model updates under a limited compute budget. It combines metric- and case-level analysis…
Recursive Harness Self-Improvement for Frontier Reasoning Data Synthesis
The preprint introduces task-harness co-evolution for recursive reasoning-data synthesis. Online self-improvement turns solver failures into reusable skills, while post-task updates revise skills, prompts, and…
Cogentic: Multi-Agent Orchestration for Automated Proof Discovery
The preprint describes an orchestrator that assigns independent provers to competing proof directions, uses specialized adversarial verification components, and promotes confirmed intermediate results into a persistent…
Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
The preprint introduces an inference-time harness in which a controller explicitly decides what partial work to reuse, whether to restart, when to stop, and which next work to dispatch. Workers perform task-level…
Shockingly Simple Self-retrospection Improves Agentic Models Without RL
The preprint introduces Retrospection-Only Fine-Tuning (ROFT), an online procedure in which an agent attempts a task, observes feedback, generates a retrospective explanation, and fine-tunes only on the explanation…
Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency
The preprint studies whether reasoning efficiency can improve without directly optimizing length or adding an inference-time stopping mechanism. It fine-tunes models to predict confidence at intermediate points in…
SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance
SAGE proposes Structural Admissibility-Guided Exploration for long-horizon reasoning under sparse rewards. It frames two failure modes—exploration bias toward locally plausible but unstable branches and compounding…
GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
GRASP is a multi-stage planning framework with separate modules for global macro-guidelines, localized strategy exploration, and independent trajectory evaluation. The preprint reports gains over direct LLM planners on…
STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution
STRETCH uses a dual-loop setup: a Scaffolder generates adaptive challenges near a model’s capability boundary, while a Learner improves solving trajectories with reinforcement learning. The abstract reports experiments…
VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
The researchers introduce VAKRA, a benchmark for evaluating the ability of agents to reason across structured APIs and document collections in enterprise settings. The benchmark includes over 8,000 executable APIs…
Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation
The research investigates self-distillation (SD), a method used in reinforcement learning with verifiable rewards, where a teacher conditioned on privileged information supplies supervision to a student. The study…
OpenCoF: Learning to Reason Through Video Generation
The paper introduces OpenCoF, a framework that includes a new dataset and a fine-tuned video model for improving Chain-of-Frame (CoF) reasoning in video generation. The authors argue that existing models lack specific…
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