InvariRank achieves permutation-invariant listwise reranking for LLM-based recommendations via a structured attention mask that blocks cross-candidate interactions and shared positional framing under RoPE, enabling stable rankings in one forward pass.
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Across 56 tasks, 9 model configurations, and 10,584 runs, LLM-generated skill files provided no reliable performance improvement over task-only prompting for data-science workflows.
LLMs judge document relevance at a level comparable to humans but frequently highlight different passages, indicating they are often not right for the right reasons and cannot fully replace human assessors.
By steering activations along directions learned from base-vs-fine-tuned disagreements, REFLEX reports 64.99 macro-F1 on RAW-FC fact-checking with only ~465 self-refined contrastive samples and no retrieval.
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
citing papers explorer
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One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
InvariRank achieves permutation-invariant listwise reranking for LLM-based recommendations via a structured attention mask that blocks cross-candidate interactions and shared positional framing under RoPE, enabling stable rankings in one forward pass.
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Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
Across 56 tasks, 9 model configurations, and 10,584 runs, LLM-generated skill files provided no reliable performance improvement over task-only prompting for data-science workflows.
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LLMs as Assessors: Right for the Right Reason?
LLMs judge document relevance at a level comparable to humans but frequently highlight different passages, indicating they are often not right for the right reasons and cannot fully replace human assessors.
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REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control
By steering activations along directions learned from base-vs-fine-tuned disagreements, REFLEX reports 64.99 macro-F1 on RAW-FC fact-checking with only ~465 self-refined contrastive samples and no retrieval.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.