Qwen3-14B
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Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
arXiv:2606.02907v1 Announce Type: new Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning types. We test this by probing Qwen3-14B on three benchmarks spanning the classical trichotomy: LogiQA 2.0 (deductive), ARC-Challenge (inductive), and $\alpha$NLI (abductive). At layer 32 of 40, linear probes achieve 100\% cross-validated accuracy with well-separated geometry (intrinsic...
Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
Announce Type: replace Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning types. We test this by probing Qwen3-14B on three benchmarks spanning the classical trichotomy: LogiQA 2.0 (deductive), ARC-Challenge (inductive), and $\alpha$NLI (abductive). At layer 32 of 40, linear probes achieve 100\% cross-validated accuracy with well-separated geometry (intrinsic dimensionalities: 20.6,...
GRPO Does Not Close the Multi-Agent Coordination Gap
Announce Type: new Abstract: We measure how well current large language models coordinate as multiple agents sharing a common resource, using the dining philosophers problem as a clean test bed. Across 630 episodes spanning seven models and three philosopher counts, four frontier closed-source systems reach mean reward 0.45 to 0.87 and Mistral-Small 24B reaches 0.83 to 0.99, while Qwen3-14B reaches 0.13 to 0.35. We then ask whether group relative policy optimization (GRPO) on rollouts from...
Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs
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Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models
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RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation
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Code Is More Than Text: Uncertainty Estimation for Code Generation
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