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Human Feedback and Eval Paper Explorer

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Total papers: 438 Search mode: keyword Shortlist (0) RSS

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

Score: 90% High protocol signal Freshness: Hot Status: Ready
Demonstrations Human EvalLlm As Judge Long Horizon General
  • 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.
Open paper
The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration

Haoyuan Xu, Chang Li, Xinyan Ma, Xianhao Ou, Zihan Zhang, Tao He · Mar 24, 2026

Citations: 0

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

Score: 90% Moderate protocol signal Freshness: Hot Status: Fallback
Automatic Metrics Tool Use Coding
  • As agent systems evolve, however, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such…
  • We comprehensively review recent progress in multi-tool LLM agents and analyzes the state of the art in this rapidly developing area.
Open paper
EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises

Ankush Agarwal, Harsh Vishwakarma, Suraj Nagaje, Chaitanya Devaguptapu · Mar 23, 2026

Citations: 0

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

Score: 90% High protocol signal Freshness: Hot Status: Fallback
Automatic Metrics Long Horizon General
  • Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints.
  • Our results demonstrate that 8B-parameter models trained within EnterpriseLab match GPT-4o's performance on complex enterprise workflows while reducing inference costs by 8-10x, and remain robust across diverse enterprise benchmarks,…
Open paper
Language Models Can Explain Visual Features via Steering

Javier Ferrando, Enrique Lopez-Cuena, Pablo Agustin Martin-Torres, Daniel Hinjos, Anna Arias-Duart, Dario Garcia-Gasulla · Mar 23, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 35% Sparse protocol signal Freshness: Hot Status: Ready
Open paper
Tiny Inference-Time Scaling with Latent Verifiers

Davide Bucciarelli, Evelyn Turri, Lorenzo Baraldi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara · Mar 23, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 35% Sparse protocol signal Freshness: Hot Status: Ready
Open paper

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