arXiv Machine Learning
Jun 25
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
Exact Feedback Is Not Control: Evaluating Text-based Closed-Loop Revision in LLMs
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arXiv AI
Aug 11
Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines
arXiv:2604. 01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction.
arXiv Computation and Language
3d ago
Draft-OPD: On-Policy Distillation for Speculative Draft Models
Draft-OPD introduces an on‑policy distillation method for speculative draft models, addressing the mismatch between supervised fine‑tuning and inference by letting the target model supervise the drafter on draft‑induced states. The approach uses target‑assisted rollouts for stable continuations and replays drafting from error positions exposed during verification, enabling the drafter to learn from both accepted and rejected proposals. Experiments demonstrate that Draft‑OPD achieves more than five‑fold lossless acceleration across diverse tasks, outperforming prior draft models such as EAGLE‑3 and DFlash by 23 % and 13 % respectively.
arXiv Computation and Language
Sep 3
User Feedback Provides a Unique Signal that LLMs Can not Detect
The paper argues that user feedback from real interactions is a valuable learning signal for Large Language Models (LLMs), contrary to recent claims that it is too noisy to use. By creating synthetic data with a clear ground truth and testing on naturalistic data, the authors show that revisions guided by user feedback fix targeted issues more often than baseline revisions. They further reveal that current evaluation methods bias against feedback‑driven improvements, as judges tend to overlook genuinely corrected responses and favor inferior baselines.
arXiv Machine Learning
3d ago
Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework
arXiv:2609.20973v1 Announce Type: cross Abstract: Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-ste...
arXiv AI
Jul 28
Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation
arXiv:2607. 22653v1 Announce Type: new Abstract: Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model.
Hugging Face Trending Papers
Jun 24
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.
arXiv AI
18h ago
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv AI
Jun 15
Can Editing 1 Neuron Fix Repetition Loops in LLMs?
arXiv:2606. 13705v1 Announce Type: cross Abstract: Yes.
arXiv Machine Learning
Jul 23
LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization
arXiv:2407. 00740v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, such as non-toxicity and logical consistency, as well as task- and situation-specific constraints.
arXiv Machine Learning
Aug 3
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
arXiv:2607. 28908v1 Announce Type: new Abstract: Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers.
arXiv AI
Jun 2
Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks
arXiv:2606. 00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.