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Open problems in Agents

Long-horizon task execution and evidence accumulation3 papers raise it · 2 address it
Agents struggle to sustain multi-step reasoning, adapt strategies, and turn intermediate results into final deliverables in real-world tasks.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Realistic evaluation of agentic learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with pre-existing knowledge rather than learning from novel, unfamiliar environments.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
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, not just appear to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
AI systems cannot reliably identify when historical context is relevant for current interactions in long-term companionship.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Scalable and realistic GUI agent training and evaluation1 paper raises it · 2 address it
High cost and scarcity of realistic interactive environments limit progress in GUI agent training and evaluation.
- 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 Useaddresses

Named by an AI from these papers' abstracts.

Open problems

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Asymptote LabsSeed · Oct 20$5.5M
GreptileSeries B+ · Oct 19$50M
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SupaHumanSeries A · Oct 19$1.5M
BoxLiteSeed · Oct 19undisclosed

Recently funded

Topics in this field

coding agents · code generation · coding agent286 papers74 this week
skills · tasks · ai308 papers57 this week
ai agents · indirect prompt injection · adversarial314 papers42 this week
tool using agents · agent systems · multi agent209 papers42 this week
recursive self improvement · harness evolution · adaptive260 papers41 this week
vision language · safety evaluations · safety alignment115 papers33 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.SEOct 16
MergeShuffle: Reconstruct Shared Libraries for Improved Performance and Security
Huaifeng Zhang, Ahmed Ali-Eldin
Debloating shared libraries has been an active research topic for years. Tools for shared library debloating rely on either static or dynamic analysis techniques to discover which parts of the shared library is used. Once these parts are found, these tools take one of two routes, either removing parts of the library that are not used, requiring reconstruction of the library; or they simply overwriting the unused functions. However, recently a broad study~\cite{brown2024broad} on debloating tools has shown that some of these tools either increase the static binary size, or are generally less effective for complex workloads. In this paper, we argue that shared-library debloaters' performance can be improved by proper reconstruction of the shared-libraries once the used functions in the library are identifies. We propose \myapp, a novel tool for reconstructing shared libraries from a given set of file ranges to eliminate unused code based on user-specified requirements, without requiring access to source code. \myapp takes as input a shared library and a set of file ranges to retain, and performs a two-phase reconstruction: merging nearby ranges to reduce loading overhead, and…
PDF arXiv
arXiv cs.AIOct 16
Multiple Turns, Short Horizons: Measuring Effective-Horizon Compression in Jailbreaks
Zhipeng Wei, Michael Lee, N. Benjamin Erichson
Multi-turn jailbreaks use feedback from a victim large language model to adapt their queries over several turns. However, once an effective trajectory has been found, it is unclear how much of the earlier query history is still needed. In this work, we study this by removing early attacker queries and running the remaining queries again in a new conversation while regenerating the victim's responses. We define the effective horizon as the shortest remaining query sequence that recovers the same attack progress. Empirically, we find that roughly half of the original query sequence is sufficient on average to recover the same attack progress. We call this phenomenon effective-horizon compression. We show that this shorter history can recover the best point reached during an unsuccessful attack and provide a better starting point for continuing the attack. It also helps identify which earlier queries should receive less learning signal during attacker training, and downweighting those queries improves performance. We further find that short reward-propagation windows preserve calibrated defense performance close to full-trajectory returns. These results show that multi-turn attacks…
PDF arXiv
arXiv cs.AIOct 16
When to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents
Chenle Chen, Yangbo Wei, Zhijie Zheng, Zhenyu Su, Qifan Wang +4
Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses. We study when retaining, compressing, or removing the same history block improves subsequent execution relative to full-history KEEP. We introduce RECAP, a framework that combines matched context interventions, execution-relational prediction, and independent policy certification. Matched environment states and rollout seeds measure success and continuation prompt-cost differences; prefix-only relations between a failure block and the current execution propose promising edits. Independent task bundles then select a policy subject to harmful-intervention and non-positive-benefit risk constraints under a fixed measurement protocol. A five-seed longitudinal case holds both the failure block and its summary fixed: compression fails at an early state and becomes a cost-saving intervention at a later state. Across five backbones, RECAP produces positive paired-success saving estimates with backbone-specific reliability-efficiency tradeoffs. On Hunyuan-A13B, it edits 43.24% of retained checkpoints while matching KEEP's observed 91.89% continuation success and reducing…
PDF arXiv
arXiv cs.GTOct 16
Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
Trung-Kiet Huynh, Dao-Sy Duy-Minh, Chi-Nguyen Tran, Phu-Hoa Pham, Phu-Quy Nguyen-Lam +2
Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes. We examine whether five production models adapt their contributions to changing risks and partner behaviour, or instead follow a relatively fixed strategy. In a collective-risk dilemma game (CRG), six players contribute to a shared pool over ten rounds; if the pool falls short of its target, a catastrophe may destroy their remaining resources. Four models contributed approximately an equal share at every positive risk level, and four of the five also contributed when risk was zero, even though contributing could not improve their payoff. Explicitly stating that only the agent's own payoff mattered reduced zero-risk contributions for most models, while a more neutral prompt did not. Several models correctly identified which action offered the higher expected payoff at different risk levels, yet their contributions changed little. Against fixed partners, no model consistently chose the payoff-maximising response, and in mixed-model groups, a single agent's shortfall in the final round could cause an otherwise successful group to fail. Payoff-based…
PDF arXiv
arXiv cs.SEOct 16
No One-Size-Fits-All: Adaptive Code Editing with Feature-Based Strategy Selection
Jun Wan, Zhongxin Liu, Dajun Chen, Wei Jiang, Yong Li +1
Large language models have shown strong potential in automating code editing (CE) tasks, yet most existing systems rely on a fixed CE strategy (i.e., one-step, search-replace, and plan-apply) across diverse scenarios. In practice, CE tasks vary widely in code context, edit locality, and semantic complexity, causing no single strategy to perform consistently well. Moreover, systematic studies on the relationship between strategies and CE tasks remain scarce, limiting further optimization of LLM-based code editing strategies. To address this gap, we construct CEBench, comprising 500 real-world CE tasks curated from SWE-bench. Then we compare three representative CE strategies (one-step, search-replace, plan-apply). Our study reveals two key findings: (1) each strategy exhibits distinct advantages for specific CE tasks, with no universal optimality; (2) strategy effectiveness is significantly influenced by four core task features: total lines of code, modification type, modification scope, and task type. Motivated by these findings, we propose Ace, an adaptive code editing framework that dynamically selects optimal CE strategies based on task features. Unlike classifier-based or…
PDF arXiv
arXiv cs.CLOct 16
Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data
Zesheng Shi, Xue Li, Chenrui Dai, Jing Li
LLMs over external data face a per-instruction tension: a single document or tool return often interleaves legitimate directives with adversarial prompt injections, and the model must follow the former while rejecting the latter. Existing defenses suppress or trust all external instructions, collapsing the safety-utility frontier and forcing a choice between over-refusal and over-compliance. We argue the missing capability is \textbf{per-instruction credit attribution} and formalize it as External-Data Instruction Classification, an identify-classify-act decision over each embedded directive. We propose \textbf{EDIC}, a decomposed reward framework for per-instruction compliance. A scene-grounded pipeline injects mixed beneficial and harmful instructions with counterfactual filtering, while a generative judge is initialized by SFT and refined with GRPO to attribute compliance at the instruction level. The judge then produces a reward that separately encourages beneficial compliance and penalizes harmful compliance for RL training. Across two backbones and five benchmarks, EDIC improves both safety and utility over the strongest prior defense, lifting harmful-rejection by up to…
PDF arXiv

Papers

Top tools here

All tools

Agno4 releases this week
FastMCP6 releases this week
n8n+25% downloads · 148.3k/wk+25.0%
DeepEval+15% downloads · 769.3k/wk+14.7%
Ragas+14% downloads · 319.9k/wk+13.6%

Topics in this field

coding agents · code generation · coding agent286 papers74 this week
skills · tasks · ai308 papers57 this week
ai agents · indirect prompt injection · adversarial314 papers42 this week
tool using agents · agent systems · multi agent209 papers42 this week
recursive self improvement · harness evolution · adaptive260 papers41 this week
vision language · safety evaluations · safety alignment115 papers33 this week

Recently funded

Market

Asymptote LabsSeed · Oct 20$5.5M
GreptileSeries B+ · Oct 19$50M
VesslaPre-seed · Oct 19$3.3M
SupaHumanSeries A · Oct 19$1.5M
BoxLiteSeed · Oct 19undisclosed

Active contributors · 90 days

Activity · 12 weeks

Open problems in Agents

Long-horizon task execution and evidence accumulation3 papers raise it · 2 address it
Agents struggle to sustain multi-step reasoning, adapt strategies, and turn intermediate results into final deliverables in real-world tasks.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Realistic evaluation of agentic learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with pre-existing knowledge rather than learning from novel, unfamiliar environments.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
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, not just appear to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
AI systems cannot reliably identify when historical context is relevant for current interactions in long-term companionship.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Scalable and realistic GUI agent training and evaluation1 paper raises it · 2 address it
High cost and scarcity of realistic interactive environments limit progress in GUI agent training and evaluation.
- 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 Useaddresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 37m ago

Agents

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

484papers this week−9.4% vs last week

All papers · 2.5k With code · 116

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2.4k papers this month

Recent papers

All papers in Research

arXiv cs.SEOct 16
MergeShuffle: Reconstruct Shared Libraries for Improved Performance and Security
Huaifeng Zhang, Ahmed Ali-Eldin
Debloating shared libraries has been an active research topic for years. Tools for shared library debloating rely on either static or dynamic analysis techniques to discover which parts of the shared library is used. Once these parts are found, these tools take one of two routes, either removing parts of the library that are not used, requiring reconstruction of the library; or they simply overwriting the unused functions. However, recently a broad study~\cite{brown2024broad} on debloating tools has shown that some of these tools either increase the static binary size, or are generally less effective for complex workloads. In this paper, we argue that shared-library debloaters' performance can be improved by proper reconstruction of the shared-libraries once the used functions in the library are identifies. We propose \myapp, a novel tool for reconstructing shared libraries from a given set of file ranges to eliminate unused code based on user-specified requirements, without requiring access to source code. \myapp takes as input a shared library and a set of file ranges to retain, and performs a two-phase reconstruction: merging nearby ranges to reduce loading overhead, and…
PDF arXiv
arXiv cs.AIOct 16
Multiple Turns, Short Horizons: Measuring Effective-Horizon Compression in Jailbreaks
Zhipeng Wei, Michael Lee, N. Benjamin Erichson
Multi-turn jailbreaks use feedback from a victim large language model to adapt their queries over several turns. However, once an effective trajectory has been found, it is unclear how much of the earlier query history is still needed. In this work, we study this by removing early attacker queries and running the remaining queries again in a new conversation while regenerating the victim's responses. We define the effective horizon as the shortest remaining query sequence that recovers the same attack progress. Empirically, we find that roughly half of the original query sequence is sufficient on average to recover the same attack progress. We call this phenomenon effective-horizon compression. We show that this shorter history can recover the best point reached during an unsuccessful attack and provide a better starting point for continuing the attack. It also helps identify which earlier queries should receive less learning signal during attacker training, and downweighting those queries improves performance. We further find that short reward-propagation windows preserve calibrated defense performance close to full-trajectory returns. These results show that multi-turn attacks…
PDF arXiv
arXiv cs.AIOct 16
When to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents
Chenle Chen, Yangbo Wei, Zhijie Zheng, Zhenyu Su, Qifan Wang +4
Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses. We study when retaining, compressing, or removing the same history block improves subsequent execution relative to full-history KEEP. We introduce RECAP, a framework that combines matched context interventions, execution-relational prediction, and independent policy certification. Matched environment states and rollout seeds measure success and continuation prompt-cost differences; prefix-only relations between a failure block and the current execution propose promising edits. Independent task bundles then select a policy subject to harmful-intervention and non-positive-benefit risk constraints under a fixed measurement protocol. A five-seed longitudinal case holds both the failure block and its summary fixed: compression fails at an early state and becomes a cost-saving intervention at a later state. Across five backbones, RECAP produces positive paired-success saving estimates with backbone-specific reliability-efficiency tradeoffs. On Hunyuan-A13B, it edits 43.24% of retained checkpoints while matching KEEP's observed 91.89% continuation success and reducing…
PDF arXiv
arXiv cs.GTOct 16
Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
Trung-Kiet Huynh, Dao-Sy Duy-Minh, Chi-Nguyen Tran, Phu-Hoa Pham, Phu-Quy Nguyen-Lam +2
Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes. We examine whether five production models adapt their contributions to changing risks and partner behaviour, or instead follow a relatively fixed strategy. In a collective-risk dilemma game (CRG), six players contribute to a shared pool over ten rounds; if the pool falls short of its target, a catastrophe may destroy their remaining resources. Four models contributed approximately an equal share at every positive risk level, and four of the five also contributed when risk was zero, even though contributing could not improve their payoff. Explicitly stating that only the agent's own payoff mattered reduced zero-risk contributions for most models, while a more neutral prompt did not. Several models correctly identified which action offered the higher expected payoff at different risk levels, yet their contributions changed little. Against fixed partners, no model consistently chose the payoff-maximising response, and in mixed-model groups, a single agent's shortfall in the final round could cause an otherwise successful group to fail. Payoff-based…
PDF arXiv
arXiv cs.SEOct 16
No One-Size-Fits-All: Adaptive Code Editing with Feature-Based Strategy Selection
Jun Wan, Zhongxin Liu, Dajun Chen, Wei Jiang, Yong Li +1
Large language models have shown strong potential in automating code editing (CE) tasks, yet most existing systems rely on a fixed CE strategy (i.e., one-step, search-replace, and plan-apply) across diverse scenarios. In practice, CE tasks vary widely in code context, edit locality, and semantic complexity, causing no single strategy to perform consistently well. Moreover, systematic studies on the relationship between strategies and CE tasks remain scarce, limiting further optimization of LLM-based code editing strategies. To address this gap, we construct CEBench, comprising 500 real-world CE tasks curated from SWE-bench. Then we compare three representative CE strategies (one-step, search-replace, plan-apply). Our study reveals two key findings: (1) each strategy exhibits distinct advantages for specific CE tasks, with no universal optimality; (2) strategy effectiveness is significantly influenced by four core task features: total lines of code, modification type, modification scope, and task type. Motivated by these findings, we propose Ace, an adaptive code editing framework that dynamically selects optimal CE strategies based on task features. Unlike classifier-based or…
PDF arXiv
arXiv cs.CLOct 16
Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data
Zesheng Shi, Xue Li, Chenrui Dai, Jing Li
LLMs over external data face a per-instruction tension: a single document or tool return often interleaves legitimate directives with adversarial prompt injections, and the model must follow the former while rejecting the latter. Existing defenses suppress or trust all external instructions, collapsing the safety-utility frontier and forcing a choice between over-refusal and over-compliance. We argue the missing capability is \textbf{per-instruction credit attribution} and formalize it as External-Data Instruction Classification, an identify-classify-act decision over each embedded directive. We propose \textbf{EDIC}, a decomposed reward framework for per-instruction compliance. A scene-grounded pipeline injects mixed beneficial and harmful instructions with counterfactual filtering, while a generative judge is initialized by SFT and refined with GRPO to attribute compliance at the instruction level. The judge then produces a reward that separately encourages beneficial compliance and penalizes harmful compliance for RL training. Across two backbones and five benchmarks, EDIC improves both safety and utility over the strongest prior defense, lifting harmful-rejection by up to…
PDF arXiv