Tracked topic
Large language models are machine learning models trained to predict and generate text and other language-based outputs.
Papers arxiv:2606.02248 Geometric Latent Reasoning Induces Shorter Generations in LLMs Published on Jun 1 Submitted by Shashi Kumar on Jun 2 Idiap Research Institute Authors: , , , , Abstract Geometric Latent Reasoning formulates…
…AI-generated summary Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM reasoning…
Papers arxiv:2606.12071 On the Limits of LLM-as-Judge for Scientific Novelty Assessment Published on Jun 10 Submitted by Soujanya Poria on Jun 10 Deep Cognition and Language Research (DeCLaRe…
Papers arxiv:2607.01233 Measuring the Gap Between Human and LLM Research Ideas Published on Jul 1 Submitted by Ziyu on Jul 6 Yale NLP Lab Authors: Ziyu Chen , , Abstract Large language…
…Uncertainty-Scaffolding Strategies for Artificial Moral Advisors in LLM-to-LLM Simulated Conversations (2026) Auditing Support Strategies in LLMs through Grounded Multi-Turn Social Simulation (2026) Evaluating the Realism of LLM-powered…
Papers arxiv:2606.29985 Are We Measuring Strategy or Phrasing? The Gap Between Surface- and Approach-Level Diversity in LLM Math Reasoning Published on Jun 29 Submitted by Lee SangMook on Jul…
…To address these limitations, we propose OmniPack, a training-free framework that coordinates structural compression before the LLM with task-relevant semantic refinement within the LLM. Before the LLM, OmniPack removes structural…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically infer which configuration…
…Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression (2026) TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization (2026) Cross-Layer Subspace Coupling for LLM Compression: A Unifying Framework and…
…Only the final-QA reader invokes an LLM. Across long-memory and long-context question-answering benchmarks, Zero-Mem achieves competitive performance while eliminating LLM calls and LLM-token consumption from memory…