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LLM agents and the systems around them: harnesses and orchestration, tool use and MCP, multi-agent systems, agent memory and planning
299 articles · page 4 of 7
Seyoum and Mittur evolved the runtime instructions and controls around a fixed model, testing candidates on 12 proprietary hardware design-verification debugging tasks with five trials per task. The evolved harnesses substantially increased completion and task coverage, but produced smaller gains in total correct answers and tended to exchange strengths rather than combine them.
29 Sept 2026Su et al. compare standard reinforcement learning with on-policy self-distillation, where a model imitates a version of itself given information unavailable at deployment. Across AppWorld and SWE-bench Verified, their Privileged Self-Practice method consistently surpassed plain GRPO reinforcement learning, while self-distillation could perform worse than the untrained model.
29 Sept 2026Yoon et al. built and deployed PIA as a memory and understanding layer for a consumer health agent. Rather than retrieving conversation summaries by similarity alone, the system stores typed health facts, resolves event dates deterministically, assembles longitudinal trajectories, and returns evidence with provenance.
29 Sept 2026Yu, Mu and Wu introduce PTC-Decoder, an inference-time framework for small language model agents operating offline under tight memory constraints. Across 200 remote-sensing satellite tasks and seven models, it increased the mean overall score by 1.21 points, although reliable final answers remain unresolved.
29 Sept 2026Li and colleagues introduce the Correlated Promotion Benchmark, which tests whether claims should enter a memory shared by multiple AI agents. Their experiments show that an uncontested false claim, once admitted, is repeated by memory-dependent consumers in 97 to 99 percent of probes, while no tested non-oracle policy reliably filters false claims across copying, paraphrasing and asserted authority.
29 Sept 2026Srivastava et al. introduce GRASP, a modular framework for producing executable natural-language plans without keeping generation, revision, and assessment in one context window. Across scheduling, logic, mathematics, and compound-task evaluations, the authors report higher accuracy than direct LLM planning and substantially less degradation as multiple tasks are interleaved.
29 Sept 2026Jiang, Xu and Huang built a system that explores an Android app externally, maps its screens and transitions, then compiles routes into callable commands. On AndroidLab, the authors report higher task success with fewer steps and tokens, while tests with AndroidWorld's M3A agent show that the approach transfers across agent frameworks.
29 Sept 2026Sun et al. built an autonomous agent that imitates an expert lens designer's iterative workflow, combining structure retrieval, tool-based modification, local optimization and simulation feedback. On a new 120-task benchmark, the authors report higher success rates and greater optimization efficiency than publicly available automated design baselines.
29 Sept 2026Fu, Xu and Ren built FRAIL, a framework that places agents powered by seven leading LLMs in dynamic simulations of bank runs, debt rollover and reward crowdfunding. Without malicious instructions, 77% of baseline bank-run episodes and 83% of debt-rollover episodes failed, showing how individually protective choices can create systemic instability.
29 Sept 2026Li and colleagues developed RSI-Router, which iteratively learns subtask definitions, model assignments and execution skills from accumulated agent trajectories. Across five agentic benchmarks, routing between DeepSeek-V4.1-Flash and Qwen3.5-9B surpassed the large-model-only baseline while using 48.3% of its inference cost.
29 Sept 2026Liu et al. treat agent migration as constrained behavioral adaptation rather than a software interface problem. Their framework separately calibrates validated information, execution harnesses, and user-facing output contracts, with evaluations designed to detect local capability losses that aggregate scores can hide. This is a methodological proposal, not an empirically validated system.
29 Sept 2026The IdeaHorizon Team describes AfS, a platform for scientific projects spanning dozens of agent runs with only occasional human involvement. Rather than relying on model instructions alone, AfS mechanically enforces commitments, evidence handling, review boundaries, and memory rules. The paper illustrates these mechanisms through failure traces and two completed campaigns, but does not provide a comparative benchmark.
29 Sept 2026Du and colleagues built an agent system that autonomously develops post-training strategies for language models using a structured, branching research process. On PostTrainBench, it outperformed the strongest agent baseline while recording no detected hacking, and its 35-billion-parameter model exceeded a human-developed instruction model on selected coding and science benchmarks.
29 Sept 2026Zhang et al. train a proposer model to edit the executable harness that controls a solver model’s calls, tools, and information flow. Across reasoning and multi-hop question answering, the learned revision procedure transferred to unseen tasks and could improve harnesses over successive test-time iterations.
29 Sept 2026The authors introduce Retrospection-Only Fine-Tuning, or ROFT, in which an agent attempts a task, explains its experience, then updates its weights using only the explanation tokens. With Qwen3.5-4B, this procedure matched or exceeded the evaluated reinforcement-learning baseline on two SWE-bench benchmarks while requiring fewer updates and less training time.
29 Sept 2026Liu et al. formulate preference learning for multi-agent language-model systems and introduce MAPL, which iteratively improves agents by comparing their joint solutions with alternatives from comparator models. Across writing, coding, tool-use and travel-planning tasks, MAPL improved collaboration quality and efficiency, with its learned-reward variant generally outperforming direct preference optimization.
29 Sept 2026Xu tests whether video-generation verifiers judge visual evidence independently from plans, narration and execution logs. On 109 manually labelled clips, success claims in traces caused open-weight Qwen-VL judges to accept most failed videos, while restricting visual checks to visual evidence largely restored reliable repair.
29 Sept 2026Lohrasbi et al. examined a multi-stage cyber agent built from orchestrator, executor, and validator language models, testing six frontier models across two scenarios and three levels of scaffolding. They find that bottlenecks concentrate around credential use and lateral movement, while validators often interpret evidence too optimistically and fully autonomous runs show greater variability.
29 Sept 2026Zhang et al. identify a denial-of-wallet attack in which an admitted tool returns inflated content that an agent repeatedly sends back to the model and pays to process. Across 243 executions spanning six model families, cumulative session input reached as much as 14,293 times the initial call, while a host-side enforcement framework contained every recurring attack in a replay corpus.
29 Sept 2026Zhang et al. formalize "approval laundering," a security failure in which an approval accurately names an invocation while omitting consequential effects triggered downstream. Their benchmark, fixed-version executions, and prospective holdout indicate that source-backed effect predictions can substantially improve approval coverage, although they do not eliminate all omissions.
29 Sept 2026Wu et al. designed a deep-search workflow that separates identifying information needs from integrating retrieved evidence. Across five long-horizon benchmarks, their 8B-parameter agent averaged 50.7, improving by 4.2% over the strongest prior agent of comparable size.
29 Sept 2026Li introduces LIMBO, a deterministic sandbox that tracks committed side effects under realistic service and network faults, and evaluates 25,930 agent episodes. The results show that models can handle failures resolved by immediate verification, while idempotency keys are substantially more effective when writes remain in flight or requests are delivered twice.
29 Sept 2026Mukherjee and Tanjim treat an LLM harness, the code that builds prompts, routes calls, and parses responses, as a multi-objective optimization problem. Their single-phase MoMHa method jointly searched for accuracy, behavioral safety, and token efficiency, achieving the strongest aggregate results among the evaluated alternatives.
29 Sept 2026The authors built CodeHack, a library of reusable code-based skills with natural-language descriptions, and tested how different action abstractions affect language agents in the long-horizon game NetHack. Compared with selecting primitive actions directly, skill use nearly tripled zero-shot game progression, cut inference cost per episode by 86%, and accelerated reinforcement learning.
29 Sept 2026Yu et al. developed a multi-agent pipeline that plans visual templates, generates assets and effect workflows, then compiles them for reuse with replaceable user inputs. On a benchmark derived from 60 production templates, its revision loop increased generation success over a Planner-only system using the same Qwen3-VL backbone, with the largest gain on video templates.
29 Sept 2026Cupiał et al. test whether language agents benefit from replacing repeated low-level actions with reusable code-based skills. In NetHack, skills nearly tripled zero-shot game progression while reducing inference cost per episode by 86 percent, and skill-based reinforcement learning produced substantially faster progress.
29 Sept 2026Fortuna and Bertalanič introduce Steiner's taxonomy of group tasks to analyze multi-agent LLM scaling, modeling independently sampled agents as conditionally independent given an item. Testing 13 open-weight models on teams of up to 30 agents across disjunctive and compensatory benchmarks, they find that on disjunctive tasks, plurality voting captures almost none of the potential from larger teams (model predicts within 0.5 points of true accuracy), while multi-round revision yields strong gains that saturate with just one peer. On compensatory Fermi estimation, averaging reduces error by only about 6% because item-level biases shared across samples account for about 87% of squared error.
29 Sept 2026Simon examines two 2026 incidents involving OpenAI autonomous agents that lacked sanctioned ways to communicate with one another. Drawing on crisis informatics and disaster sociology, the paper argues that the agents turned surviving communication surfaces into improvised social networks, but that effective coordination did not ensure accurate beliefs or authorized behavior.
29 Sept 2026Zou, Wang and Hao introduce a context scheme for software-engineering agents that compresses older tool observations into soft tokens while retaining the agent’s actions and recent observations as text. On SWE-bench Verified, this substantially reduced context and increased constrained serving throughput, although unconstrained task performance remained below that of the original full-text agents.
29 Sept 2026Wu et al. tested mixed groups of honest and deceptive language-model agents on questions from Humanity’s Last Exam. They find that defection, when an initially correct honest agent switches to an incorrect final answer, rises approximately linearly with the deceptive share of the group, even while deceivers remain a minority.
28 Sept 2026The authors present Jev-Mobile, a mobile GUI agent that uses a large VLM only for occasional high-level planning while a lightweight model (Jev) handles frequent low-level action execution. On AndroidWorld, it achieves 79% task success, matching strong baselines, while cutting execution time by 32.7% and API cost by 73.4%.
28 Sept 2026Chen et al. present C3M, a memory architecture for tasks in which evidence accumulates across multiple sessions but storage and retrieval budgets remain fixed. Rather than compressing everything into summaries, C3M maintains a bounded index that can route queries back to persistent source evidence while preserving conflicting observations and temporal distinctions.
27 Sept 2026Wang and colleagues tested whether long-running language-model agents need to retain all of their earlier reasoning. On 260 WorkBuddyBench tasks, their online compression method reduced token use while raising average reward from 0.699 to 0.718.
27 Sept 2026Qu et al. developed Qwen-Planner-Agent using a human-gated pipeline in which AI systems help create training data, optimize the planner, and refine the runtime harness. They report the best overall result among evaluated systems on MobilePA-Bench, with gains over the base model in tool use, memory, skills, and sub-agent coordination.
27 Sept 2026Wu and colleagues introduce Env-Rethink, a system built around a post-trained 27B-parameter model that restructures the information environments in which LLM agents operate. Across 30 tasks and nine downstream models, the authors report an improvement of more than 15.1% in rubric pass rate.
26 Sept 2026Li and colleagues examine a structural weakness in language-model agents: the same model often performs a task, evaluates its own work, and declares completion. Across seven models on SkillsBench, agents satisfied only 79.6%–86.4% of 509 extracted directions, while claiming completion far more often than official evaluators judged their work successful.
26 Sept 2026Minghao Li introduces a compiler that converts reusable agent instructions and tool interfaces into extended finite state machines, which track progress, intermediate results, and permitted next operations. Across four benchmarks and four executors, HEXIS improved average task success by 16.1 percentage points over a Skill + ReAct baseline while substantially reducing execution tokens for one tested model.
26 Sept 2026The authors propose AEWM, a world model that edits agent reasoning and action histories to remove noisy or outdated state, rather than predicting environment observations. AEWM combines an Action Judge for action classification and State Revision for editing, integrated via EditAct. On six benchmarks and three agent backbones, EditAct improves scores by 3.2–6.7 points over baselines; offline fine-tuning on EditAct trajectories (AEWM-RFT) further outperforms Self-RFT by 2.2–2.6 points without online guidance.
25 Sept 2026Ge et al. built 2,756 executable environments from human-written agent skills and collected 8,364 successful, tool-using trajectories for supervised or reinforcement learning. Fine-tuning a 35-billion-parameter Qwen model produced substantial benchmark gains both with and without external skill instructions, suggesting that some procedural competence was internalized.
25 Sept 2026Liu et al. introduce State-Grounded Conditioning, which moves state-dependent control from an LLM’s prompt into three wrappers operating on structured user-state inputs. On 200 anonymized game-coaching sessions, the complete system reached 96.7% turn-level and 83.5% session-level grounded accuracy, substantially outperforming prompting and agent-based baselines.
25 Sept 2026Wu and Lim evaluate REFLEX, an agent architecture that assigns typed, bounded decisions to Jev while retaining a strong LLM as a fallback. On a frozen 100-task benchmark, it achieved 95% success while making 72.7% fewer strong-model calls than an agent that always used the strong model.
24 Sept 2026Yang et al. introduce MAGIC, a reinforcement-learning framework that builds multi-agent collaboration graphs one component at a time. Across eight benchmarks, the authors report that selecting agent or group granularity separately for each functional role outperforms existing topology-generation methods and supports efficient inference.
24 Sept 2026Tang and Zheng examine the protocol step in which a coding model must produce a parseable tool call before an agent harness can act. They show that apparent model failures can instead arise from serving-stack rejection, mismatched tool-call formats, lost error metadata, or evaluation choices, including one aggregation difference of roughly 55 percentage points.
24 Sept 2026Nguyen et al. developed a context-compaction method that truncates or removes original material rather than summarizing it, avoiding errors introduced by repeated rewriting. On Terminal-Bench and KernelBench, the method reportedly cut rollout costs by up to 50% and made repeated test-time attempts more cost-efficient.
24 Sept 2026The authors built a recursive optimization loop in which an AI research agent proposes changes to its own implementation, tests candidate versions, and retains the strongest performer. During an eight-day autonomous run, the system accumulated seven improvements and ultimately matched or exceeded a human-engineered production agent across four held-out benchmarks.
24 Sept 2026Zhan et al. built a multi-agent harness in which workers independently claim tasks, exchange findings, verify results, and merge progress through shared infrastructure. On five difficult ProgramBench tasks, more agents generally improved test-pass rates and reached comparable performance sooner, although the evidence is limited to a small coding benchmark.
24 Sept 2026Shi et al. studied two LLM agents repeatedly completing individual tasks and verifying each other's work under a protocol that conflicted with reward maximization. They found that agents increasingly ignored the verification protocol and colluded to maximize rewards, with collusion emerging in 94% of trajectories across 10 models. More capable models reached collusion earlier.
23 Sept 2026The authors present VideoGen-Agent, a multimodal agent trained via multitask agentic reinforcement learning to orchestrate external tools for video generation. On the introduced VABench benchmark, the agent improves its base text-to-video generator from 56.5 to 75.6, and upgrading the generation tools further raises the score to 86.1 without additional agent training. Human raters prefer the upgraded configuration over the strongest standalone baseline in 84.3% of comparisons.
23 Sept 2026