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Open problems in Agents

Long-horizon task completion with exploration and verification3 papers raise it · 2 address it
Agents struggle to accumulate evidence, adjust strategies, and verify results over long, realistic workflows.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often fail to deliver agents that truly meet the specified requirements rather than just appearing to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Realistic evaluation of agent learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with known rules rather than learning novel, counterintuitive rules through interaction.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
Agents cannot reliably identify when historical context is relevant, leading to missed or incorrect responses.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible GUI agent stacks2 papers raise it · 2 address it
Frontier GUI agents are often closed, limiting systematic study and community collaboration.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Tolan AI Companion AppStage not given · Oct 23$30M
hexafarmsStage not given · Oct 23$5.4M
CygridPre-seed · Oct 23$3M
SunniesSeed · Oct 23$318.9k
Warp SciencesStage not given · Oct 22$215M

Recently funded

Topics in this field

coding agents · code generation · coding agent318 papers73 this week
recursive self improvement · harness evolution · adaptive278 papers41 this week
skills · tasks · ai321 papers40 this week
agent · agentic · llm agents179 papers39 this week
tool using agents · agent systems · multi agent223 papers36 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.AIOct 10
SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition
Yuxuan Liu, Zhaochen Su, Hangyu Guo, Shijue Huang, Zhixiong Zhang +9
Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evaluation is to expand task requirements and their dependencies while keeping tasks executable and outcomes verifiable. We introduce SkillHorizon, a benchmark that constructs long-horizon tasks through skill composition in seven business environments. Its generators use domain-specific rules to turn reusable skill procedures into concrete business activities. These activities are connected through input-output dependencies, shared resources, and state updates, allowing workload and coordination demands to grow together. Successful reference executions validate task feasibility and measure interaction workload, while programmatic evaluators assess local completion and whole-task success. We evaluate five language models on 1,050 tasks with reference interaction workloads ranging from 64K to 1M tokens. Across these scales, mean local-goal completion falls from 90.4% to 46.8%, as completed work fails to keep pace with growing requirements. At larger workloads, agents more often submit unsuccessful final answers with substantial work…
PDF arXiv
arXiv cs.SECodeOct 10
Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering
Samuele Doria, Stefano De Rosa, Ruoyu Wang, Zion Leonahenahe Basque, Eleonora Losiouk +1
Can an autonomous AI agent reverse engineer a binary as well as a skilled human analyst? Do they adopt the same strategy, and can agents scale to real-world programs? We compare human and LLM-based software reverse engineering (SRE): nine models tackle two compact static-analysis challenges previously solved by human analysts, scored with a common understanding rubric, and we record their complete interactions with IDA Pro. This lets us compare not only what humans and agents recover, but also how they investigate a binary. The best agents (GPT-5.5 Pro, Claude Opus 4.8, and Kimi K3) achieve statistically human-competitive understanding on both tasks, while weaker models remain far behind. Ablations show that the gap depends on code representation and workflow demands, not on model capability alone: open-weight models improve substantially with richer code representations, whereas frontier models show no significant change. Similar success rates hide different workflows. Humans repeatedly revisit functions and follow local program structure; strong agents jump across the binary, retrieve information broadly, and rarely return to inspected code, a behavior we call greedy fetching.…
PDF arXiv Code
arXiv cs.AIOct 10
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents
Kounianhua Du, Jianxing Liu, Shuai Shao, Ke Li, Xing Sun +1
The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a standardized interface. Learning to use this ecosystem entails a hierarchy of capabilities: tool-format skills, server-level transfer, and cross-server composition. We present MCPDisCo, an ecosystem-scale reinforcement learning framework for MCP-native agents. Using a Thought-Markov Decision Process (Thought-MDP), the framework explicitly represents internal reasoning alongside routing, execution, and termination and trains on real-server, long-horizon interactions spanning 70 MCP servers and 527 tools across six domains. We study the reasoning--tool seesaw, a joint-learning failure mode in which improvement in one objective can accompany deterioration in the other. To mitigate potential interference, MCPDisCo combines token-masked reward attribution with bidirectional gradient surgery: the former separates direct supervision for reasoning and action spans, while the latter removes conflicting components from both objective gradients in shared parameters. MCPDisCo achieves higher success with fewer chain-of-thought tokens. On the…
PDF arXiv
arXiv cs.LGOct 10
TLA-Verus: Generating and Solving Temporal Proof Obligations with LLM Agents
Kseniia Strelbytska, Balázs Szilágyi, Anna Mészáros, Ferenc Huszár
The TLA+ (temporal logic of actions) formal specification toolkit has seen widespread adoption in modeling modern distributed software systems including databases, storage systems, ledgers and coordination protocols. This popularity is despite its two significant limitations for software verification: Firstly, TLA+ offers no way to detect drift between software implementation and mathematical specification; secondly, TLC and TLAPS cannot provide satisfactory proofs of arbitrary temporal logic properties. We address the second limitation and take a first step towards the first by translating TLA+ specifications into Verus, an auto-active verifier built for Rust, via both a deterministic transpiler and an agentic translator. We demonstrate that our LLM-based pipeline is able to generate proof obligations from TLA+ specifications at scale, and to produce fully machine checkable proofs of both safety and liveness properties. Since temporal logic reasoning in Verus is a largely new task for LLMs, our work yields a valuable evaluation dataset, which we use to map out current generation model capabilities in Verus theorem proving.
PDF arXiv
arXiv cs.AIOct 10
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications
Zhengxi Li, Junyi Zhou, Qiyuan Zhang, Chuhan Wu, Fuyuan Lyu +4
Evaluating an agent's ability to strictly satisfy diverse and procedurally complex requirements is essential to determining its practical reliability. Conducting such an evaluation demands agentic tasks that reconcile broad domain coverage with deep procedural complexity. Existing paradigms face a fundamental trade-off: expert-curated benchmarks achieve procedural depth but suffer from domain diversity bottlenecks, whereas crowdsourced benchmarks span diverse scenarios but lack execution rigor. To resolve this tension, we present SkillForgeBench, which leverages agent skills as unifying execution blueprints across three co-designed stages: (1) Resource Preparation uses domain-balanced skills to guide artifact retrieval, establishing broad workflow coverage; (2) Skill-driven Task Synthesis employs skills as procedural anchors to partition artifact roles, synthesize naturalistic task instructions, and package sandboxed scenarios for deep procedural execution; and (3) Holistic Evaluation Design translates skill-embedded requirements into fine-grained completion and process rubrics to enable strict requirement-level scoring. Comprising 842 sandboxed tasks across five major…
PDF arXiv
arXiv cs.AIOct 10
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions
Junyi Zhou, Yufei Wang, Qiyuan Zhang, Chuhan Wu, Zhengxi Li +5
Developing terminal-based agents for long-horizon execution demands synthetic tasks that tightly couple complex instructions with authentic, interactive environments. However, current automated synthesis suffers from a fundamental asymmetry: instruction-oriented methods fabricate low-fidelity, synthetic environments, whereas resource-oriented methods treat real artifacts merely as static inputs, degrading task instructions into shallow, trivial operations. To address this limitation, we introduce WorkBorne, a synthesis framework that uses artifact transitions as the central primitive for modeling the full life-cycle of agentic tasks. In WorkBorne, each transition specifies who performs the task, which artifacts anchor the initial state and intended outcome, and the corresponding relationship between them. Specifically, WorkBorne first constructs coherent and diverse artifact corpora from iterative web retrieval, then discovers non-trivial transitions by identifying meaningful input-output artifact assemblies and the relations that connect them. Each discovered transition is finally instantiated into grounded agentic tasks. We construct 1,061 artifact corpora and 7,882 task…
PDF arXiv

Papers

Top tools here

All tools

QwenPaw+1266% downloads · 39k/wk+1266.0%
billion-context+259% downloads · 1.1M/wk+258.8%
jcode+233% downloads · 25.5k/wk+233.1%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
TanStack AI+68% downloads · 1M/wk+67.7%

Topics in this field

coding agents · code generation · coding agent318 papers73 this week
recursive self improvement · harness evolution · adaptive278 papers41 this week
skills · tasks · ai321 papers40 this week
agent · agentic · llm agents179 papers39 this week
tool using agents · agent systems · multi agent223 papers36 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week

Recently funded

Market

Tolan AI Companion AppStage not given · Oct 23$30M
hexafarmsStage not given · Oct 23$5.4M
CygridPre-seed · Oct 23$3M
SunniesSeed · Oct 23$318.9k
Warp SciencesStage not given · Oct 22$215M

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 10
SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition
Yuxuan Liu, Zhaochen Su, Hangyu Guo, Shijue Huang, Zhixiong Zhang +9
Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evaluation is to expand task requirements and their dependencies while keeping tasks executable and outcomes verifiable. We introduce SkillHorizon, a benchmark that constructs long-horizon tasks through skill composition in seven business environments. Its generators use domain-specific rules to turn reusable skill procedures into concrete business activities. These activities are connected through input-output dependencies, shared resources, and state updates, allowing workload and coordination demands to grow together. Successful reference executions validate task feasibility and measure interaction workload, while programmatic evaluators assess local completion and whole-task success. We evaluate five language models on 1,050 tasks with reference interaction workloads ranging from 64K to 1M tokens. Across these scales, mean local-goal completion falls from 90.4% to 46.8%, as completed work fails to keep pace with growing requirements. At larger workloads, agents more often submit unsuccessful final answers with substantial work…
PDF arXiv
arXiv cs.SECodeOct 10
Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering
Samuele Doria, Stefano De Rosa, Ruoyu Wang, Zion Leonahenahe Basque, Eleonora Losiouk +1
Can an autonomous AI agent reverse engineer a binary as well as a skilled human analyst? Do they adopt the same strategy, and can agents scale to real-world programs? We compare human and LLM-based software reverse engineering (SRE): nine models tackle two compact static-analysis challenges previously solved by human analysts, scored with a common understanding rubric, and we record their complete interactions with IDA Pro. This lets us compare not only what humans and agents recover, but also how they investigate a binary. The best agents (GPT-5.5 Pro, Claude Opus 4.8, and Kimi K3) achieve statistically human-competitive understanding on both tasks, while weaker models remain far behind. Ablations show that the gap depends on code representation and workflow demands, not on model capability alone: open-weight models improve substantially with richer code representations, whereas frontier models show no significant change. Similar success rates hide different workflows. Humans repeatedly revisit functions and follow local program structure; strong agents jump across the binary, retrieve information broadly, and rarely return to inspected code, a behavior we call greedy fetching.…
PDF arXiv Code
arXiv cs.AIOct 10
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents
Kounianhua Du, Jianxing Liu, Shuai Shao, Ke Li, Xing Sun +1
The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a standardized interface. Learning to use this ecosystem entails a hierarchy of capabilities: tool-format skills, server-level transfer, and cross-server composition. We present MCPDisCo, an ecosystem-scale reinforcement learning framework for MCP-native agents. Using a Thought-Markov Decision Process (Thought-MDP), the framework explicitly represents internal reasoning alongside routing, execution, and termination and trains on real-server, long-horizon interactions spanning 70 MCP servers and 527 tools across six domains. We study the reasoning--tool seesaw, a joint-learning failure mode in which improvement in one objective can accompany deterioration in the other. To mitigate potential interference, MCPDisCo combines token-masked reward attribution with bidirectional gradient surgery: the former separates direct supervision for reasoning and action spans, while the latter removes conflicting components from both objective gradients in shared parameters. MCPDisCo achieves higher success with fewer chain-of-thought tokens. On the…
PDF arXiv
arXiv cs.LGOct 10
TLA-Verus: Generating and Solving Temporal Proof Obligations with LLM Agents
Kseniia Strelbytska, Balázs Szilágyi, Anna Mészáros, Ferenc Huszár
The TLA+ (temporal logic of actions) formal specification toolkit has seen widespread adoption in modeling modern distributed software systems including databases, storage systems, ledgers and coordination protocols. This popularity is despite its two significant limitations for software verification: Firstly, TLA+ offers no way to detect drift between software implementation and mathematical specification; secondly, TLC and TLAPS cannot provide satisfactory proofs of arbitrary temporal logic properties. We address the second limitation and take a first step towards the first by translating TLA+ specifications into Verus, an auto-active verifier built for Rust, via both a deterministic transpiler and an agentic translator. We demonstrate that our LLM-based pipeline is able to generate proof obligations from TLA+ specifications at scale, and to produce fully machine checkable proofs of both safety and liveness properties. Since temporal logic reasoning in Verus is a largely new task for LLMs, our work yields a valuable evaluation dataset, which we use to map out current generation model capabilities in Verus theorem proving.
PDF arXiv
arXiv cs.AIOct 10
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications
Zhengxi Li, Junyi Zhou, Qiyuan Zhang, Chuhan Wu, Fuyuan Lyu +4
Evaluating an agent's ability to strictly satisfy diverse and procedurally complex requirements is essential to determining its practical reliability. Conducting such an evaluation demands agentic tasks that reconcile broad domain coverage with deep procedural complexity. Existing paradigms face a fundamental trade-off: expert-curated benchmarks achieve procedural depth but suffer from domain diversity bottlenecks, whereas crowdsourced benchmarks span diverse scenarios but lack execution rigor. To resolve this tension, we present SkillForgeBench, which leverages agent skills as unifying execution blueprints across three co-designed stages: (1) Resource Preparation uses domain-balanced skills to guide artifact retrieval, establishing broad workflow coverage; (2) Skill-driven Task Synthesis employs skills as procedural anchors to partition artifact roles, synthesize naturalistic task instructions, and package sandboxed scenarios for deep procedural execution; and (3) Holistic Evaluation Design translates skill-embedded requirements into fine-grained completion and process rubrics to enable strict requirement-level scoring. Comprising 842 sandboxed tasks across five major…
PDF arXiv
arXiv cs.AIOct 10
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions
Junyi Zhou, Yufei Wang, Qiyuan Zhang, Chuhan Wu, Zhengxi Li +5
Developing terminal-based agents for long-horizon execution demands synthetic tasks that tightly couple complex instructions with authentic, interactive environments. However, current automated synthesis suffers from a fundamental asymmetry: instruction-oriented methods fabricate low-fidelity, synthetic environments, whereas resource-oriented methods treat real artifacts merely as static inputs, degrading task instructions into shallow, trivial operations. To address this limitation, we introduce WorkBorne, a synthesis framework that uses artifact transitions as the central primitive for modeling the full life-cycle of agentic tasks. In WorkBorne, each transition specifies who performs the task, which artifacts anchor the initial state and intended outcome, and the corresponding relationship between them. Specifically, WorkBorne first constructs coherent and diverse artifact corpora from iterative web retrieval, then discovers non-trivial transitions by identifying meaningful input-output artifact assemblies and the relations that connect them. Each discovered transition is finally instantiated into grounded agentic tasks. We construct 1,061 artifact corpora and 7,882 task…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 15m ago

Agents

LLM agents: planning, tool use, multi-agent systems, computer/web agents and agent benchmarks.

455papers this week−23.5% vs last week

All papers · 2.7k With code · 141

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2.4k papers this month

Open problems in Agents

Long-horizon task completion with exploration and verification3 papers raise it · 2 address it
Agents struggle to accumulate evidence, adjust strategies, and verify results over long, realistic workflows.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often fail to deliver agents that truly meet the specified requirements rather than just appearing to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Realistic evaluation of agent learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with known rules rather than learning novel, counterintuitive rules through interaction.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
Agents cannot reliably identify when historical context is relevant, leading to missed or incorrect responses.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible GUI agent stacks2 papers raise it · 2 address it
Frontier GUI agents are often closed, limiting systematic study and community collaboration.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses

Named by an AI from these papers' abstracts.