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This paper studies hidden states in long horizon language model agents, revealing that memory compression and recall needs are encoded before actions. It proposes the PaMER framework to reduce context consumption while maintaining task performance through state-guided compression and evidence retrieval.
DRACO is a reinforcement learning method that dynamically generates rubrics and redistributes trajectory scores to improve long-horizon agent performance without verifiers.
ContextPilot-14B is a Qwen3-14B checkpoint that teaches language-model agents proactive context management via fine-grained reinforcement learning for long-horizon tasks.
This paper argues that long-horizon AI agent reliability is lacking despite better models, as seen on WeaveBench with only 41.2% pass rate, and proposes the LongHorizon-Harness to manage task state for improved performance.
ProgramBench is a benchmark for agents to reproduce real software behavior from scratch. Factory is announced as the most advanced reverse-engineering system with breakthroughs in model-agnostic long-horizon agents.
The author discusses insights from rebuilding self-service onboarding as agentic flows, emphasizing how long-horizon agents that combine deterministic task management with non-deterministic LLM actions improve reliability and reduce hallucinations in enterprise AI deployments.
ECHO is a cognitively inspired, auditable memory architecture for long-horizon agents, evaluated on benchmarks like LoCoMo and LongMemEval-S with high retrieval performance.
The paper introduces Recuris, a recursive memory architecture that improves long-horizon agent success by tracking progress and guiding skill selection through localized, validation-gated updates.
This paper presents a holistic evaluation of memory substrates for memory-augmented LLM agents, finding that no single substrate dominates across all regimes and advocating for adaptive substrate routing to optimize performance in different operating conditions.
LongRCA Bench introduces a benchmark for diagnosing failures in long-horizon agent trajectories, and the RCTA method improves responsible role and root-cause step attribution.
Introduces Governed Persistent Memory (GPM), a bitemporal state-transition model for auditable long-horizon agent memory with source-bound semantics and fail-closed release, validated on benchmarks and sealed evaluations.
This paper introduces MESA, a framework that dynamically selects and fuses a query-adaptive subset of multiple structural memory views for long-horizon agents, achieving 8.5% accuracy improvement over the strongest baseline while using 41% fewer evidence tokens.
This arXiv survey (1,547 papers, 2024-2026) systematically maps the field of long-horizon LLM agents, disambiguating long-horizon, long-context, and long-term memory, and organizing research into six lifecycle categories while identifying the core 'horizon gap' and open measurement problems.
This empirical study investigates how recurrent context compression affects long-horizon agent behavior, showing that compression can weaken recent interaction influence and cause instability. The authors introduce TRACE, a verifier-guided framework that improves compression reliability and performance on AppWorld.
MemPrism proposes a task-conditioned relational memory framework that separates persistent experience storage from decision-time working memory, enabling long-horizon agents to dynamically construct relational views for improved performance and reduced token usage.
StepReflect reformulates per-step GUI reflection for mobile agents as supervised structured prediction, achieving higher transition accuracy than GPT-5.2 on AndroidWorld while reducing API costs.
Presents a verification instrument for long-horizon agents that structurally separates commitment drift from binding drift, using a deterministic executive and pre-registered predictions. Reports ablation results showing commitment mechanism removal flips goal abandonment from 0 to 1 while binding error stays flat, though task efficacy is null on ARC-AGI-3.
A new survey from Renmin University reviews nearly 1,000 studies on long-horizon AI agents, arguing that reliable long-horizon intelligence depends on the whole model-harness system, not just larger context windows or stronger models.
This paper introduces JarvisBench, a benchmark for evaluating a continuous, real-time spoken mediation layer in long-horizon AI agent workflows, and presents a modular Jarvis prototype tested on WildClaw tasks with various LLM-based worker agents.
TRACE introduces a turn-level reward assignment method using frozen reference model log-probabilities and temporal-difference learning to address credit assignment in long-horizon agentic tasks, achieving significant improvements in search benchmarks without critic or process labels.