arXiv AI
Jul 20
CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data
arXiv:2607. 16122v1 Announce Type: new Abstract: Evaluations should do more than measure a models current performance.
TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
TIDE (Tree-Induced Diagnostic Evaluation) transforms diagnostic evaluation into a behavior-driven decision-tree induction process, enabling fine-grained capability diagnosis of large language models. By recursively building a hierarchical capability tree from benchmark samples and model scores, TIDE identifies nuanced contrasts between models that aggregate metrics miss—such as Claude Opus 4.6 outperforming GPT-5.4 on microeconomic market reasoning while falling behind on contract law reasoning. The method leverages LLM judges to propose splits and uses behavior-based information gain to select them, demonstrating its effectiveness across five models and four benchmarks.
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arXiv AI
Oct 16
Evaluating Model Capabilities After Benchmark Saturation
The paper examines whether transcripts of model solutions can reveal capabilities after benchmark scores saturate. By simulating saturation with historical data, the authors test protocols that use only correctly solved transcripts to predict performance on unsaturated items. Pairwise judging of transcripts outperforms other methods, reducing prediction error by 5.3×, though its effectiveness can vary across distribution shifts.
arXiv AI
Aug 17
Split the Labor: Separating Evidence Interpretation from Decision Aggregation
arXiv:2608. 14509v1 Announce Type: new Abstract: Systems that ask a language model to reach a conclusion from many sources usually concatenate them into one prompt.
arXiv AI
Jul 10
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
arXiv:2601. 22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design.
arXiv AI
Jun 11
Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
arXiv Machine Learning
Oct 8
Latent Performance Profiling of Large Language Models
arXiv:2605.30018v3 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of the...
arXiv Computation and Language
Sep 11
Rethinking Verbalized Confidence for LLM-as-a-Judge: A Compatibility Shift on Post-2025 Proprietary Models
The paper argues that verbalized confidence—once viewed as overconfident and coarse—has become the preferred soft‑scoring method for LLM‑as‑a‑Judge on top‑tier proprietary models released after 2025. Experiments on SummEval, AggreFact, and HelpSteer2 across up to 18 LLMs show that log‑probabilities are no longer the best signal, and that adding an overconfidence advisory and self‑debate further improves calibration and robustness. The authors note that these enhancements incur little accuracy loss on post‑2025 models but do affect pre‑2025 ones, highlighting a compatibility shift in how confidence should be measured.
arXiv Machine Learning
Jul 1
White-Box Sensitivity Auditing with Steering Vectors
arXiv:2601. 16398v3 Announce Type: replace-cross Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators.
arXiv AI
Aug 6
Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools
arXiv:2608. 04719v1 Announce Type: new Abstract: Agent evaluations tell us that a model picked the wrong tool, but rarely why.
arXiv AI
Jul 16
Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation
arXiv:2603. 00546v2 Announce Type: replace Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm across various domains.
arXiv Computation and Language
Aug 27
Anchoring Bias in LLM-as-a-Judge Systems: Prior Scores Compromise Evaluation Independence
The study investigates how prior scores influence large language model (LLM) judgments in the LLM-as-a-Judge paradigm. By testing three prompt conditions—no metadata, revision framing, and anchored metadata containing prior scores—the authors find that prior scores systematically bias evaluations, shifting ratings toward those scores across 192,000 attempts. The bias also affects categorical decisions, blocking 48% of error corrections and flipping 10.18% of correct judgments, and is not mitigated by Chain-of-Thought or a warning, underscoring the need for careful context engineering.
arXiv AI
Jun 29
CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.