- AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding · Mar 22, 2026 · Citations: 0
Demonstrations Human EvalLlm As Judge Long Horizon
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…
- \$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li, Jun Bai, Hao Chen · Mar 9, 2026 · Citations: 0
Rubric Rating Automatic Metrics Tool Use
To this end, we introduce \OneMillion-Bench \OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios.
- TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen · Apr 8, 2026 · Citations: 0
Red Team Automatic Metrics Long Horizon
As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.
- Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing
Anmol Gulati, Sahil Sen, Waqar Sarguroh, Kevin Paul · Mar 6, 2026 · Citations: 0
Human EvalAutomatic Metrics Long Horizon
We introduce Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval with an iterative tool-calling loop, supporting end-to-end Excel workflows from complex analysis…
- Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought
Xinghao Zhao · Mar 19, 2026 · Citations: 0
Automatic Metrics Long Horizon
Chain-of-thought (CoT) reasoning improves LLM accuracy, yet detecting failures cheaply remains elusive.
- ReDAct: Uncertainty-Aware Deferral for LLM Agents
Dzianis Piatrashyn, Nikita Kotelevskii, Kirill Grishchenkov, Nikita Glazkov, Ivan Nasonov · Apr 8, 2026 · Citations: 0
Simulation Env Long Horizon
Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems.
- DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents
Snehasis Mukhopadhyay · Mar 14, 2026 · Citations: 0
Automatic MetricsSimulation Env Long Horizon
We introduce DECEPTGUARD, a unified framework that systematically compares three monitoring regimes: black-box monitors (actions and outputs only), CoT-aware monitors (additionally observing the agent's chain-of-thought reasoning trace),…
- LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
Koki Itai, Shunichi Hasegawa, Yuta Yamamoto, Gouki Minegishi, Masaki Otsuki · Mar 6, 2026 · Citations: 0
Llm As JudgeAutomatic Metrics Long Horizon
To bridge the gap between existing evaluations and practical use, we introduce LIT-RAGBench (the Logic, Integration, Table, Reasoning, and Abstention RAG Generator Benchmark), which defines five categories: Integration, Reasoning, Logic,…
- DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling
Shicheng Liu, Yucheng Jiang, Sajid Farook, Camila Nicollier Sanchez, David Fernando Castro Pena · Apr 7, 2026 · Citations: 0
Human Eval Long Horizon
Deep research with Large Language Model (LLM) agents is emerging as a powerful paradigm for multi-step information discovery, synthesis, and analysis.
- Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents
Khushal Sethi · Apr 9, 2026 · Citations: 0
Automatic Metrics Long Horizon
We introduce TrACE (Trajectorical Adaptive Compute via agrEement), a training-free controller that allocates LLM calls adaptively across agent timesteps by measuring inter-rollout action agreement.
- MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents
Shu Wang, Edwin Yu, Oscar Love, Tom Zhang, Tom Wong · Apr 6, 2026 · Citations: 0
Automatic Metrics Long Horizon
Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning, yet standard context-window and retrieval-augmented generation (RAG) pipelines degrade over…
- OSCAR: Orchestrated Self-verification and Cross-path Refinement
Yash Shah, Abhijit Chakraborty, Naresh Kumar Devulapally, Vishnu Lokhande, Vivek Gupta · Apr 2, 2026 · Citations: 0
Automatic Metrics Long Horizon
We introduce a suite of trajectory-level assessments, including a cross-chain divergence-at-hallucination (CDH) metric, for principled comparison of localization methods.
- S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models
Jack Young · Apr 1, 2026 · Citations: 0
Automatic Metrics Long Horizon
Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.
- Asymmetric Actor-Critic for Multi-turn LLM Agents
Shuli Jiang, Zhaoyang Zhang, Yi Zhang, Shuo Yang, Wei Xia · Mar 31, 2026 · Citations: 0
Automatic Metrics Long Horizon
In many real-world applications, agents must succeed in one-shot settings where retries are impossible.
- 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
Automatic Metrics Long Horizon
Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints.
- Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes
Deepon Halder, Raj Dabre · Mar 15, 2026 · Citations: 0
Automatic Metrics Long Horizon
Empirical validation on GPQA and GSM8K benchmarks indicates that Top-b significantly reduces generation entropy and inter-decoding variance while maintaining competitive reasoning accuracy, effectively approximating a self-regulating…
- Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni, Chao Yan, Katherine Brown · Mar 9, 2026 · Citations: 0
Automatic Metrics Long Horizon
Recent self-consistency-based approaches further improve accuracy but require sampling and aggregating multiple reasoning trajectories, leading to substantial additional computational overhead.
- PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
Zhifei Xie, Zongzheng Hu, Fangda Ye, Xin Zhang, Haobo Chai · Apr 9, 2026 · Citations: 0
Automatic Metrics Long Horizon
Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambiguity, precision and real-time constraints.
- Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency
Guan-Ting Lin, Chen Chen, Zhehuai Chen, Hung-yi Lee · Apr 6, 2026 · Citations: 0
Automatic Metrics Tool Use
We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use.
- SkillX: Automatically Constructing Skill Knowledge Bases for Agents
Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0
Automatic Metrics Long Horizon
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…
- $\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
Muyu He, Adit Jain, Anand Kumar, Vincent Tu, Soumyadeep Bakshi · Apr 1, 2026 · Citations: 0
Automatic Metrics Long Horizon
As LLM agents tackle increasingly complex tasks, a critical question is whether they can maintain strategic coherence over long horizons: planning under uncertainty, learning from delayed feedback, and adapting when early mistakes compound.
- Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards
Cheng Jiayang, Xin Liu, Zhihan Zhang, Haoyang Wen, Zixuan Zhang · Mar 25, 2026 · Citations: 0
Automatic Metrics Long Horizon
We present a framework addressing both challenges.
- Effective Strategies for Asynchronous Software Engineering Agents
Jiayi Geng, Graham Neubig · Mar 23, 2026 · Citations: 0
Automatic Metrics Long Horizon
Inspired by these collaboration primitives, we introduce Centralized Asynchronous Isolated Delegation (CAID), a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous…