Papers

Field

Top tools here

All tools

jcode+233% downloads · 25.5k/wk+233.1%
billion-context+259% downloads · 1.1M/wk+258.8%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
TanStack AI+68% downloads · 1M/wk+67.7%
Vibe-Trading+100% downloads · 3.1k/wk+100.3%

Top tools here

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, open-ended tasks in real-world settings.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- Into the Unknown: Benchmarking AI agents on open scientific research problemsraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often produce systems that appear to meet requirements but fail to deliver the intended behavior on held-out cases.
- 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
Agents cannot reliably identify when historical information is relevant, leading to missed or misapplied context.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Transition-aware safety for GUI agents1 paper raises it · 1 addresses it
Safety checks that only inspect screens can be bypassed by reusing legitimate screens in unexpected transitions.
- Right Screen, Wrong Transition: World Models as Verifiers for GUI Agentsraises · addresses
Learning from experience in unfamiliar environments1 paper raises it · 1 addresses it
Agents often rely on pretrained knowledge rather than acquiring new knowledge through interaction in novel settings.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Market

Hongxiong AISeries A · Oct 25undisclosed
Pathos AISeries B+ · Oct 23$237M
Tolan AI Companion AppStage not given · Oct 23$30M
hexafarmsStage not given · Oct 23$5.4M
CygridPre-seed · Oct 23$3M

Recently funded

Topics in this field

coding agents · code generation · coding agent318 papers62 this week
recursive self improvement · harness evolution · adaptive278 papers37 this week
tool using agents · agent systems · multi agent223 papers35 this week
skills · tasks · ai321 papers33 this week
agent · agentic · llm agents179 papers33 this week
ai agents · indirect prompt injection · adversarial328 papers32 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.CROct 7
Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy
Georgios Koutidis, Nikolaos Kekatos, Tom Nianios, Alexios Lekidis
Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts. Large Language Models (LLMs) are increasingly proposed as reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts. This coupling introduces a new risk: a single adversarial alert can become a remote code path through the LLM's reasoning, leading it to recommend an action the SOC then executes. We present a constrained-action architecture with two coordinated layers: (i) a SIEM/XDR control plane that grounds remediation in correlated host events and confines the LLM's output to a closed intent vocabulary whose templated commands are executed by thin endpoint agents, backstopped by an argument validator; and (ii) a NeMo-Guardrails proxy that wraps the SOC-analyst LLM with input- and output-rail policies, evaluated out-of-the-box against a SOC-specific adversarial corpus we release. The stock proxy lifts injection recall from 25.0% to 94.5% at a 0.1% false-positive rate, and a live red-team exercise confirms…
PDF arXiv
arXiv cs.HCOct 7
Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance
Sebastian Cochinescu
We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about attachment, trust, perceived mind, or machine interiority. Partiality has two components: caring, defined as allocation of a finite interaction surplus above a guaranteed per-user service floor, and particularity, defined as a per-user state estimate accumulated from relationship history on fixed inference machinery. We implement both as a persistence layer over one open-weight base model and evaluate them on constructed multi-session relationships with known hidden-state schedules. A four-channel divergence compares known-user and stranger conditions on identical probes with paired generation seeds; the stranger prompt is not length-matched, and one channel (initiative) is an allocator output rather than generated behavior. The full mechanism reproduces the ordinal shape, but not the magnitude, of an analytically specified curve and is the only arm satisfying the protocol-defined joint signature on the calibrated battery. The initial mechanism fails probe-quality equivalence because relationship content reduces topical relevance. A…
PDF arXiv
arXiv cs.AICodeOct 7▲ 131
LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets
Jun Zhao, Leiming Fu, Yanbo Wen, Yiding Wang, Xuantong Liu +7
Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a closed-loop interaction between agent behavior and changing external conditions. We introduce LiveMACEBench, a process-aware benchmark that uses live financial markets as a naturally evolving testbed for persistent LLM agents. Five frontier LLMs operate along continuous trajectories under matched Tool Use, Persistent Memory, Rule Following, and Multi-Agent Collaboration configurations. We evaluate them through both realized outcomes and mechanism-specific diagnostics derived from complete decision traces. Across 30 days of live evaluation, we find a pronounced outcome-capability gap: realized returns often diverge from capability-specific measurements, and similar outcomes can arise from markedly different patterns of mechanism use. Trace-level diagnostics further expose distinct bottlenecks across capabilities, demonstrating that mechanism access, effective mechanism use, and downstream performance are not interchangeable measures of agent capability. LiveMACEBench makes this distinction measurable, turning…
PDF arXiv Code
arXiv cs.SEOct 7
Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking
Yongjian Tang, Linhan Li, Thomas Runkler
Existing LLM-based approaches for software Requirements Engineering (RE) typically rely on basic prompting strategies or rudimentary agent collaboration, under-utilizing the full potential of multi-agent systems. Meanwhile, available datasets focus on isolated subtasks, such as requirements extraction, classification, and completeness detection, leaving the absence of an end-to-end RE benchmark that spans from requirements elicitation to generation. We present Agent4RE - a self-refining multi-agent RE system that orchestrates specialized agents and incorporates two iterative improvement loops. To support evaluation, we construct RE-E2E - a real-world dataset built from human-written requirement specifications, enabling end-to-end assessment of RE workflows. Building on this foundation, we further propose two enhanced Agent4RE versions that incorporate either autonomous self-refinement or structured human feedback, and analyze their strengths and limitations across different scenarios. Evaluation on 8 Large Language Models (LLMs) demonstrates that all three Agent4RE variants consistently outperform a domain-context-augmented prompting baseline by average 8% in text-based metrics.…
PDF arXiv
arXiv cs.AIOct 7
Self-Evolve With a Reference:Anchored Training of Tool-Integrated Agents
Wenjie Liao, Liangjie Zhao, Zehong Cao
Self-evolving tool-integrated agents learn from tasks and feedback generated within their own training loop. A Curriculum Agent generates tasks, while an Executor Agent learns from self-consistency signals through reinforcement learning. However, relying solely on the current Executor for feedback has two limitations: group-relative advantages vanish under full consensus, while uncertainty-based curriculum rewards favor disagreement without showing whether the generated tasks support further learning. These limitations motivate an additional reference beyond the current Executor. We propose \textit{AnchorLoop}, which introduces a frozen copy of the previous iteration's Executor as a historical reference and reuses it on both sides of the training loop. For the Executor, the anchor provides a cross-reference advantage that evaluates current outputs against both current and historical majority answers. For the Curriculum, it provides an agreement-based reference based on differences in sampled majority agreement. Since the Executor and anchor have identical parameters during Curriculum training, this comparison serves as a proxy for task selection rather than evidence of…
PDF arXiv
arXiv cs.AICodeOct 7▲ 17
SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
Yuyao Ge, Yiwei Wang, Yuchen He, Baolong Bi, Lingrui Mei +3
Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applicability condition over task types. However, retaining every skill indiscriminately as the policy improves lets obsolete or harmful entries accumulate and mislead the agent. We propose SkillForge, an agentic RL method that compiles and evolves the skill library through a fitness-driven skill lifecycle of trial, active, stable, and retired states, so that the skills and the model co-evolve throughout training. A pre-RL evaluation phase first uses the base model's own rollouts to pre-retire low-fitness skills, yielding a filtered library that then seeds supervised fine-tuning. Reinforcement learning takes over from this checkpoint, and at each iteration selective retirement, stabilization, and LLM-guided mutation continue to forge the skill library alongside policy optimization. Across multiple interactive agent benchmarks, SkillForge achieves the highest aggregate success rate, delivering up to 7.8% relative improvement over the strongest baseline while keeping the skill library compact throughout…
PDF arXiv Code

Papers

Top tools here

All tools

jcode+233% downloads · 25.5k/wk+233.1%
billion-context+259% downloads · 1.1M/wk+258.8%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
TanStack AI+68% downloads · 1M/wk+67.7%
Vibe-Trading+100% downloads · 3.1k/wk+100.3%

Recently funded

Market

Hongxiong AISeries A · Oct 25undisclosed
Pathos AISeries B+ · Oct 23$237M
Tolan AI Companion AppStage not given · Oct 23$30M
hexafarmsStage not given · Oct 23$5.4M
CygridPre-seed · Oct 23$3M

Topics in this field

coding agents · code generation · coding agent318 papers62 this week
recursive self improvement · harness evolution · adaptive278 papers37 this week
tool using agents · agent systems · multi agent223 papers35 this week
skills · tasks · ai321 papers33 this week
agent · agentic · llm agents179 papers33 this week
ai agents · indirect prompt injection · adversarial328 papers32 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CROct 7
Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy
Georgios Koutidis, Nikolaos Kekatos, Tom Nianios, Alexios Lekidis
Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts. Large Language Models (LLMs) are increasingly proposed as reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts. This coupling introduces a new risk: a single adversarial alert can become a remote code path through the LLM's reasoning, leading it to recommend an action the SOC then executes. We present a constrained-action architecture with two coordinated layers: (i) a SIEM/XDR control plane that grounds remediation in correlated host events and confines the LLM's output to a closed intent vocabulary whose templated commands are executed by thin endpoint agents, backstopped by an argument validator; and (ii) a NeMo-Guardrails proxy that wraps the SOC-analyst LLM with input- and output-rail policies, evaluated out-of-the-box against a SOC-specific adversarial corpus we release. The stock proxy lifts injection recall from 25.0% to 94.5% at a 0.1% false-positive rate, and a live red-team exercise confirms…
PDF arXiv
arXiv cs.HCOct 7
Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance
Sebastian Cochinescu
We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about attachment, trust, perceived mind, or machine interiority. Partiality has two components: caring, defined as allocation of a finite interaction surplus above a guaranteed per-user service floor, and particularity, defined as a per-user state estimate accumulated from relationship history on fixed inference machinery. We implement both as a persistence layer over one open-weight base model and evaluate them on constructed multi-session relationships with known hidden-state schedules. A four-channel divergence compares known-user and stranger conditions on identical probes with paired generation seeds; the stranger prompt is not length-matched, and one channel (initiative) is an allocator output rather than generated behavior. The full mechanism reproduces the ordinal shape, but not the magnitude, of an analytically specified curve and is the only arm satisfying the protocol-defined joint signature on the calibrated battery. The initial mechanism fails probe-quality equivalence because relationship content reduces topical relevance. A…
PDF arXiv
arXiv cs.AICodeOct 7▲ 131
LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets
Jun Zhao, Leiming Fu, Yanbo Wen, Yiding Wang, Xuantong Liu +7
Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a closed-loop interaction between agent behavior and changing external conditions. We introduce LiveMACEBench, a process-aware benchmark that uses live financial markets as a naturally evolving testbed for persistent LLM agents. Five frontier LLMs operate along continuous trajectories under matched Tool Use, Persistent Memory, Rule Following, and Multi-Agent Collaboration configurations. We evaluate them through both realized outcomes and mechanism-specific diagnostics derived from complete decision traces. Across 30 days of live evaluation, we find a pronounced outcome-capability gap: realized returns often diverge from capability-specific measurements, and similar outcomes can arise from markedly different patterns of mechanism use. Trace-level diagnostics further expose distinct bottlenecks across capabilities, demonstrating that mechanism access, effective mechanism use, and downstream performance are not interchangeable measures of agent capability. LiveMACEBench makes this distinction measurable, turning…
PDF arXiv Code
arXiv cs.SEOct 7
Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking
Yongjian Tang, Linhan Li, Thomas Runkler
Existing LLM-based approaches for software Requirements Engineering (RE) typically rely on basic prompting strategies or rudimentary agent collaboration, under-utilizing the full potential of multi-agent systems. Meanwhile, available datasets focus on isolated subtasks, such as requirements extraction, classification, and completeness detection, leaving the absence of an end-to-end RE benchmark that spans from requirements elicitation to generation. We present Agent4RE - a self-refining multi-agent RE system that orchestrates specialized agents and incorporates two iterative improvement loops. To support evaluation, we construct RE-E2E - a real-world dataset built from human-written requirement specifications, enabling end-to-end assessment of RE workflows. Building on this foundation, we further propose two enhanced Agent4RE versions that incorporate either autonomous self-refinement or structured human feedback, and analyze their strengths and limitations across different scenarios. Evaluation on 8 Large Language Models (LLMs) demonstrates that all three Agent4RE variants consistently outperform a domain-context-augmented prompting baseline by average 8% in text-based metrics.…
PDF arXiv
arXiv cs.AIOct 7
Self-Evolve With a Reference:Anchored Training of Tool-Integrated Agents
Wenjie Liao, Liangjie Zhao, Zehong Cao
Self-evolving tool-integrated agents learn from tasks and feedback generated within their own training loop. A Curriculum Agent generates tasks, while an Executor Agent learns from self-consistency signals through reinforcement learning. However, relying solely on the current Executor for feedback has two limitations: group-relative advantages vanish under full consensus, while uncertainty-based curriculum rewards favor disagreement without showing whether the generated tasks support further learning. These limitations motivate an additional reference beyond the current Executor. We propose \textit{AnchorLoop}, which introduces a frozen copy of the previous iteration's Executor as a historical reference and reuses it on both sides of the training loop. For the Executor, the anchor provides a cross-reference advantage that evaluates current outputs against both current and historical majority answers. For the Curriculum, it provides an agreement-based reference based on differences in sampled majority agreement. Since the Executor and anchor have identical parameters during Curriculum training, this comparison serves as a proxy for task selection rather than evidence of…
PDF arXiv
arXiv cs.AICodeOct 7▲ 17
SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
Yuyao Ge, Yiwei Wang, Yuchen He, Baolong Bi, Lingrui Mei +3
Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applicability condition over task types. However, retaining every skill indiscriminately as the policy improves lets obsolete or harmful entries accumulate and mislead the agent. We propose SkillForge, an agentic RL method that compiles and evolves the skill library through a fitness-driven skill lifecycle of trial, active, stable, and retired states, so that the skills and the model co-evolve throughout training. A pre-RL evaluation phase first uses the base model's own rollouts to pre-retire low-fitness skills, yielding a filtered library that then seeds supervised fine-tuning. Reinforcement learning takes over from this checkpoint, and at each iteration selective retirement, stabilization, and LLM-guided mutation continue to forge the skill library alongside policy optimization. Across multiple interactive agent benchmarks, SkillForge achieves the highest aggregate success rate, delivering up to 7.8% relative improvement over the strongest baseline while keeping the skill library compact throughout…
PDF arXiv Code

Activity · 12 weeks

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, open-ended tasks in real-world settings.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- Into the Unknown: Benchmarking AI agents on open scientific research problemsraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often produce systems that appear to meet requirements but fail to deliver the intended behavior on held-out cases.
- 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
Agents cannot reliably identify when historical information is relevant, leading to missed or misapplied context.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Transition-aware safety for GUI agents1 paper raises it · 1 addresses it
Safety checks that only inspect screens can be bypassed by reusing legitimate screens in unexpected transitions.
- Right Screen, Wrong Transition: World Models as Verifiers for GUI Agentsraises · addresses
Learning from experience in unfamiliar environments1 paper raises it · 1 addresses it
Agents often rely on pretrained knowledge rather than acquiring new knowledge through interaction in novel settings.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 32m ago

Agents

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

395papers this week−34.7% vs last week

All papers · 2.7k With code · 143

Newest Most relevant

2.4k papers this month