Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 1/1 across title and protocol fields.

• In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…
• PubMed Reasoner with a GPT-4o backbone achieves 78.32% accuracy on PubMedQA, slightly surpassing human experts, and showing consistent gains on MMLU Clinical Knowledge.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
• We introduce AgentHER, a framework that recovers this lost training signal by adapting the Hindsight Experience Replay (HER; Andrychowicz et al., 2017) principle to natural-language agent trajectories for offline data augmentation.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• We evaluate on HotpotQA-RAG v3, a controlled multi-hop benchmark, under an artifact-aware protocol (shortcut baselines, counterfactual swaps, no-oracle checks, GPT-4o audits).
• Calibrated SURE-RAG reaches 0.9075 Macro-F1 (0.8951 +/- 0.0069), substantially above DeBERTa mean-pooling (0.6516) and a GPT-4o judge (0.7284), while matching a strong but opaque concat cross-encoder (0.8888 +/- 0.0109) with full…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Existing alignment paradigms remain limited in capturing the pluralistic nature of human values.
• The trained Qwen2.5-3B-Instruct model surpasses a 20B GPT-OSS baseline with a 37.4 percent relative accuracy gain on a Natural Language Inference benchmark, and also outperforms a modular architecture baseline with a 19.1 percent relative…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely MENT.
• To mitigate these limitations, we propose RATE, a novel agentic translation evaluation framework, centered by a reflective Core Agent that dynamically invokes specialized sub-agents.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Unfortunately, there is still a large gap between real-world MCP usage and current evaluation: they typically assume single-server settings and directly inject tools into the model's context, bypassing the challenges of large-scale…
• We benchmark 12 state-of-the-art LLMs and observe a substantial performance gap: while Claude-Sonnet-4 reaches 78.95% task success, most models achieve only 30-50%.

Match reason: Matched by broad semantic/index fallback.

• Existing approaches, such as LLM-as-a-judge or process reward models, incur substantial computational overhead and suffer from estimation noise.
• Experiments with multiple models of varying scales across seven long-context benchmarks show that TAMTRL consistently outperforms strong baselines, demonstrating its effectiveness.

Match reason: Matched by broad semantic/index fallback.

• The reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks.
• In this work, we present a fully automated framework designed to address these challenges by enabling scalable, high-quality translation of datasets and benchmarks.

Match reason: Matched by broad semantic/index fallback.

• To bridge this gap, we present DeEscalWild, a novel benchmark dataset curated from a multi-stage pipeline of in-the-wild police-civilian interactions extracted from publicly available video repositories.
• Extensive experiments demonstrate that SLMs fine-tuned on this data significantly outperform their base counterparts across ROUGE-L, BLEU-4, METEOR, BERTScore, Realism Score, and human evaluation metrics.

Match reason: Matched by broad semantic/index fallback.

• Despite rapid progress in language models, we still lack a clear way to understand how their abilities in these interpersonal domains compare to those of humans.
• We introduce HEART, the first-ever framework that directly compares humans and LLMs on the same multi-turn emotional-support conversations.

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• Finally, we assess the level of obtained WMPCs via human and LLM-as-a-judge evaluations.
• Nonetheless, our structural and qualitative evaluation indicates that both generation strategies can yield high-quality WMPCs.

Match reason: Matched by broad semantic/index fallback.

• To address this, we present CircuitLM, a multi-agent pipeline that translates user prompts into structured, visually interpretable CircuitJSON schematics.
• To systematically assess performance, we deploy a rigorous dual-layered evaluation methodology: a deterministic Electrical Rule Checking (ERC) engine categorizes topological faults by strict severity (Critical, Major, Minor, Warning), while…