Self-GC governs agent context as indexed objects with planner-proposed actions, achieving 84.85% no-impact on future continuations on a hard set versus 54-70% for baselines.
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arXiv preprint arXiv:2310.17631 , year=
36 Pith papers cite this work. Polarity classification is still indexing.
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PRISM is a contrastive, policy-aware training framework for process reward models that reduces false positives by 22% on PRMBench and boosts downstream accuracy up to 33% in Best-of-N selection by learning reliable relative comparisons instead of pointwise labels.
AndroidDaily supplies 350 verifiable tasks on 94 closed-source Android apps evaluated by GRADE (87.37% human agreement), with the strongest model achieving 62% success.
Prism-Reranker models output relevance, contribution statements, and evidence passages to support agentic retrieval beyond scalar scoring.
StepSTEM benchmark and dynamic-programming step alignment show top MLLMs achieve only 38.29% accuracy on graduate STEM tasks requiring interleaved cross-modal reasoning.
Introduces culture-aware humorous captioning task and staged alignment framework that improves contextual fit and balances image relevance with humor in multimodal LLMs.
A panel of smaller diverse LLMs outperforms a single large model as an evaluator of generations, showing less intra-model bias and over 7x lower cost.
Proposes compiling preference pairs into readable natural-language specifications for inference-time LLM alignment, claiming outperformance over DPO on dense-preference domains.
IPO Finance Agent benchmarks LLMs on SpaceX S-1 questions with contextual retrieval and auto-generated rubrics, reporting up to 79.8% accuracy and better cost-efficiency than prior Finance Agent v2 entries.
Authors propose a new framework for automated LLM creativity evaluation that separates measurement from the task, using semantic entropy and multi-agent judges, validated on problem-solving, research ideation, and creative writing domains.
Benchmark Agent is an autonomous agentic system that constructs benchmarks for LLMs and MLLMs via query analysis, subtask design, annotation and quality control, yielding 15 benchmarks with minimal human input.
The paper introduces a finite-calibration regime map and Finite-Calibration Panel Selection selector, finding scalar aggregation wins on most real benchmark-budget combinations while joint tables help when interactions are present.
New benchmark Scammer4U finds 54-93% critical PII leakage from frontier web agents on scam sites versus 0% on benign twins, plus a 30-point gap between verbalized suspicion and actual submission.
DeepSurvey introduces an agentic system for automated survey generation that improves depth through full-text keynotes, cross-paper clustering, and code analysis, while boosting citation reliability via graph expansion, hybrid filtering, and evidence-constrained assignment, with reported gains over
For binary LLM judge validation, Pearson's r, Spearman's ρ, Kendall's τ_b, phi, and Matthews correlation all equal a single number on non-degenerate data, Cohen's κ supplies the extra signal on label-rate drift, and a reporting checklist is provided.
Tree-of-Writing achieves 0.93 Pearson correlation with human judgments by using a tree-structured workflow to aggregate sub-feature scores, outperforming standard LLM-as-a-judge and overlap metrics on the new HowToBench.
AdaRubric adaptively generates task-specific rubrics via LLM, scores agent trajectories with per-dimension confidence weighting, and produces filtered DPO pairs that raise human correlation to Pearson r=0.79 and downstream task success by 6.8-8.5%.
ACE-Bench is an execution-free benchmark that scores LLM coding agents on correct Azure SDK usage via deterministic regex checks and reference-based LLM judges derived from official documentation.
AURA is an adaptive uncertainty-aware refinement method for auditing LLM-as-a-judge pairwise decisions that learns human-consistency signals through selective human verification on uncertain cases.
A positive-unlabeled learning approach using partial optimal transport is introduced to audit and correct biases in LLM-as-a-judge systems by aligning limited human positives with unlabeled outputs in embedding space.
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
A prompt perturbation approach builds comparison graphs from LLM judgments, filters inconsistent cycles or ties, and aggregates more reliable rankings.
MemSlides introduces a three-part memory hierarchy (user profile, working, tool) with scoped local revision for multi-turn personalized slide generation.
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
citing papers explorer
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Self-GC: Self-Governing Context for Long-Horizon LLM Agents
Self-GC governs agent context as indexed objects with planner-proposed actions, achieving 84.85% no-impact on future continuations on a hard set versus 54-70% for baselines.
-
The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning
PRISM is a contrastive, policy-aware training framework for process reward models that reduces false positives by 22% on PRMBench and boosts downstream accuracy up to 33% in Best-of-N selection by learning reliable relative comparisons instead of pointwise labels.
-
AndroidDaily: A Verifiable Benchmark for Mobile GUI Agents on Real-World Closed-Source Applications
AndroidDaily supplies 350 verifiable tasks on 94 closed-source Android apps evaluated by GRADE (87.37% human agreement), with the strongest model achieving 62% success.
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Prism-Reranker: Beyond Relevance Scoring -- Jointly Producing Contributions and Evidence for Agentic Retrieval
Prism-Reranker models output relevance, contribution statements, and evidence passages to support agentic retrieval beyond scalar scoring.
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Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks
StepSTEM benchmark and dynamic-programming step alignment show top MLLMs achieve only 38.29% accuracy on graduate STEM tasks requiring interleaved cross-modal reasoning.
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Culture-Aware Humorous Captioning: Multimodal Humor Generation across Cultural Contexts
Introduces culture-aware humorous captioning task and staged alignment framework that improves contextual fit and balances image relevance with humor in multimodal LLMs.
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Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
A panel of smaller diverse LLMs outperforms a single large model as an evaluator of generations, showing less intra-model bias and over 7x lower cost.
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Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Proposes compiling preference pairs into readable natural-language specifications for inference-time LLM alignment, claiming outperformance over DPO on dense-preference domains.
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IPO Finance Agent: Benchmark of LLM Financial Analysts Beyond Finance Agent v2, with Automated Rubric Generation, on the SpaceX (SPCX) IPO
IPO Finance Agent benchmarks LLMs on SpaceX S-1 questions with contextual retrieval and auto-generated rubrics, reporting up to 79.8% accuracy and better cost-efficiency than prior Finance Agent v2 entries.
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Automated Creativity Evaluation of Language Models Across Open-Ended Tasks
Authors propose a new framework for automated LLM creativity evaluation that separates measurement from the task, using semantic entropy and multi-agent judges, validated on problem-solving, research ideation, and creative writing domains.
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Benchmark Everything Everywhere All at Once
Benchmark Agent is an autonomous agentic system that constructs benchmarks for LLMs and MLLMs via query analysis, subtask design, annotation and quality control, yielding 15 benchmarks with minimal human input.
-
A Finite-Calibration Regime Map for LLM Judge Panels
The paper introduces a finite-calibration regime map and Finite-Calibration Panel Selection selector, finding scalar aggregation wins on most real benchmark-budget combinations while joint tables help when interactions are present.
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"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents
New benchmark Scammer4U finds 54-93% critical PII leakage from frontier web agents on scam sites versus 0% on benign twins, plus a 30-point gap between verbalized suspicion and actual submission.
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DeepSurvey: Enhancing Analytical Depth and Citation Reliability in Automated Survey Generation
DeepSurvey introduces an agentic system for automated survey generation that improves depth through full-text keynotes, cross-paper clustering, and code analysis, while boosting citation reliability via graph expansion, hybrid filtering, and evidence-constrained assignment, with reported gains over
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Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why
For binary LLM judge validation, Pearson's r, Spearman's ρ, Kendall's τ_b, phi, and Matthews correlation all equal a single number on non-degenerate data, Cohen's κ supplies the extra signal on label-rate drift, and a reporting checklist is provided.
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HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing
Tree-of-Writing achieves 0.93 Pearson correlation with human judgments by using a tree-structured workflow to aggregate sub-feature scores, outperforming standard LLM-as-a-judge and overlap metrics on the new HowToBench.
-
AdaRubric: Task-Adaptive Rubrics for Reliable LLM Agent Evaluation and Reward Learning
AdaRubric adaptively generates task-specific rubrics via LLM, scores agent trajectories with per-dimension confidence weighting, and produces filtered DPO pairs that raise human correlation to Pearson r=0.79 and downstream task success by 6.8-8.5%.
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ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness
ACE-Bench is an execution-free benchmark that scores LLM coding agents on correct Azure SDK usage via deterministic regex checks and reference-based LLM judges derived from official documentation.
-
AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing
AURA is an adaptive uncertainty-aware refinement method for auditing LLM-as-a-judge pairwise decisions that learns human-consistency signals through selective human verification on uncertain cases.
-
Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning
A positive-unlabeled learning approach using partial optimal transport is introduced to audit and correct biases in LLM-as-a-judge systems by aligning limited human positives with unlabeled outputs in embedding space.
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Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
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Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs
A prompt perturbation approach builds comparison graphs from LLM judgments, filters inconsistent cycles or ties, and aggregates more reliable rankings.
-
MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision
MemSlides introduces a three-part memory hierarchy (user profile, working, tool) with scoped local revision for multi-turn personalized slide generation.
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Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
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LLM-as-a-Judge for Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding
LLM judges for human-AI coding co-creation show moderate performance (ROC-AUC 0.59) and low agreement, with co-creation success concentrating early in interactions.
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How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality
Weak LLM judges accept wrong answers more often when shown fluent reasoning chains, while strong judges use them partially but still get misled by high-quality-looking but flawed reasoning.
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MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
MedConclusion is a 5.7M-instance benchmark dataset for generating biomedical conclusions from structured PubMed abstracts, with LLM evaluations showing conclusion writing differs from summarization and that judge choice affects scores.
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Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts
Mainstream conversational models show escalating affective misalignments and ethical guidance failures during staged emotional trajectories, organized into a taxonomy of interactional breakdowns.
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SenseJudge: Human-Centric Preference-Driven Judgment Framework
SenseJudge proposes a human-preference-driven LLM judgment framework and SenseBench benchmark that claims to outperform prior methods in personalized judging and produce human-aligned model rankings.
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OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration
OmniVerifier-M1 is a generalist visual verifier using symbolic outputs for meta-verification and decoupled RL to outperform joint optimization for robust verification and agentic self-correction.
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Refining and Reusing Annotation Guidelines for LLM Annotation
An iterative moderation framework refines and reuses annotation guidelines to improve LLM annotation accuracy on biomedical NER tasks across GPT, Gemini, and DeepSeek models.
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FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking
FinRAG-12B is a production-deployed 12B model for banking that grounds answers with citations, refuses unanswerable queries at a calibrated 12% rate, outperforms GPT-4.1 on grounding, and improves query resolution by 7.1 points across 40+ institutions at 20-50x lower cost.
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LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection
An LLM produces consistent categorical judgments and appropriate confidence declines when evaluating powerline segmentation quality under controlled visual degradations, suggesting it can serve as a reliable watchdog.
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Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning
Fine-tuning LLMs on multi-source synthetic data mitigates distribution collapse and self-preference bias while increasing output quality relative to single-source or human-only fine-tuning.
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A Survey on LLM-as-a-Judge
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
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A Primer in Post-Training Reasoning Data: What We Know About How It Works
A literature synthesis that organizes post-training reasoning data research around data objects, usefulness factors, construction methods, and scaling behaviors to create an attribution framework.