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Research Editor selected

EvoMCP: Co-evolving MCP agents and training data

EvoMCP is a framework that enables Model Context Protocol (MCP) agents to improve through a closed loop of task evolution, trajectory evolution, and supervised fine-tuning. It assesses the student's capabilities from rollout feedback, translates learning targets into executable tasks by adapting seed tasks, and uses reusable skills to recover failed teacher rollouts. Experiments on Toolathlon, MCPMark, and MCP-Atlas show absolute gains of 16.5%, 10.2%, and 12.6% respectively over the initial student.

Why it matters: Introduces a novel agent-data mutual evolution method that adapts training data to the student's evolving capabilities, addressing a key limitation of static supervised fine-tuning for MCP agents.

arxiv4d agoScore 88

Model Context Protocolagent trainingdata evolution

Research Editor selected

KEEPER: Learning Control-Sufficient Textual Memory for Long-Horizon LLM Agents

The paper introduces KEEPER, a framework that analyzes auxiliary objectives for recursive textual memory in LLM agents, identifying self-consistency bias where incorrect claims persist in memory. It compares four supervision targets and finds reward-related prediction achieves the highest task performance before and after offline reinforcement learning. A claim-level analysis reveals error persistence despite corrective observations, motivating evaluation of error persistence and valid-information retention alongside task outcomes.

Why it matters: Provides a concrete, recent research contribution addressing a critical failure mode in long-horizon LLM agent memory, with practical implications for agent reliability.

arxiv4d agoScore 85

LLM agentsmemory managementreinforcement learning

Research

Multi-Test Nation Bias Benchmarking of LLMs with Explicit Unbiased Baselines

Researchers introduce a benchmark using 722 official UN Security Council records and five complementary tests to measure nation-level bias in LLMs, including opinion ranking, association sorting, vote simulation, opposition attribution, and framing. They define explicit unbiased reference statuses for each test, including action-based pseudo-baselines derived from observable voting behavior for tests where ground truth is difficult to specify. Benchmarking GPT-4o-mini, Llama 3.3-70B, Mistral-22B-Small, and Qwen3-30B across the five permanent UNSC members reveals a test-agnostic macro-pattern: outputs lean toward the opposition side for China and Russia and toward the supportive side for the U.K., the U.S., and France. The authors argue that international-relations-facing LLM deployments need auditing with grounded, multi-test benchmarks tied to explicit reference points.

arxiv4d agoScore 76

LLM evaluationnation-level biasUN Security Council

Research Editor selected

Sparse Text Diffusion under Dual Budgets: Context Connectivity and Expert Activation

The paper introduces DualBudget, a sparse text diffusion method that separately budgets attention graph edges and expert activations. It combines local, dilated, and global attention with conditional expert computation. Experiments show gains over baselines up to 16K context length with a 20% cost reduction, though performance degrades at longer lengths.

Why it matters: Provides a principled approach to balancing efficiency and quality in long-context diffusion models.

arxiv4d agoScore 85

sparse text diffusiondual budgetslong-context modeling

Research Editor selected

Specification-Guided Generation for Large Language Models

A new neuro-symbolic decoding framework enforces formal specifications during LLM generation, not just syntax. It offers a forward-only variant guaranteeing specification satisfaction and a backtracking variant that revises earlier choices to avoid forcing unsuitable values. The method is model-agnostic, requires no retraining, and reduces repeated inference compared to rejection sampling on configuration and constraint-solving benchmarks.

Why it matters: Introduces a practical, model-agnostic approach to enforce formal specifications in LLM outputs, improving reliability for structured generation tasks.

arxiv4d agoScore 88

large language modelsconstrained generationneuro-symbolic AI

Research Editor selected

ORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement

ORION introduces a hierarchical orchestrator that uses two LLM agents to translate service intents and generate abstract placement plans for 6G network slices across administrative domains. A cooperative multi-agent RL coordinator selects cross-domain partitions while per-domain agents handle function placement. The key innovation is an outcome-driven episodic memory that records network conditions, proposed plans, and verified feasibility outcomes, feeding successes and failures back to the planner. In controlled experiments, this memory improves admission rates by 4.6–11.5 percentage points over the same planner without memory.

Why it matters: Provides a concrete, measurable improvement in 6G network orchestration by combining LLM planning with RL and a novel outcome-driven memory mechanism.

arxiv4d agoScore 88

ORION6G slice placementoutcome-driven memory

Research Editor selected

Verify–Backtrack Loop: Structured Precondition Recovery for Long-Horizon Language Agents

The paper introduces the Verify–Backtrack Loop (VBL), a training-free extension of ReAct that uses a bounded goal stack and dual-horizon verification to diagnose and recover from precondition failures in long-horizon tasks. VBL jointly checks whether an active goal contributes to the terminal goal and whether it is feasible in the current state, then triggers explicit operations like Push, Pop, retry, or BacktrackTo. Evaluated on AppWorld, ALFWorld, and ScienceWorld with Qwen3.5-9B, GPT-4o, and DeepSeek-V3.2, VBL improves task success by up to 22.07 percentage points over ReAct, and ablations confirm that both verification and diagnosis are complementary.

Why it matters: Provides a structured, training-free method for improving failure recovery in language agents, with strong empirical gains across multiple benchmarks and backbones.

arxiv4d agoScore 88

language agentstask decompositionfailure recovery

Research Editor selected

Patch-Based Deep Kernel Learning for Data Assimilation and Bias Correction with Extremely Sparse Observations

The paper proposes a hybrid framework that couples a CNN with a learnable-kernel Gaussian process to jointly perform data assimilation and bias correction from extremely sparse observations. A patch-based strategy reduces exact-GP training cost from O(N^3) to O(N) for a fixed patch size, while inference cost depends on the number of observations rather than the full grid. Experiments on sea surface temperature and salinity with synthetic and real sparse in-situ observations show improvements over 3D-Var, a classical GP, and a competing deep-learning model, with inference time reduced by more than 98% relative to 3D-Var.

Why it matters: It offers a concrete AI method for uncertainty-aware data assimilation and bias correction, with efficiency and accuracy claims relevant to weather and ocean modeling.

arxiv4d agoScore 83

data assimilationGaussian processesdeep learning

Research Editor selected

Pre-Registered Audit of 25 Cognitive-Architecture Modules in an LLM Agent System

The paper introduces a four-question static audit protocol with line-number evidence and call-chain tracing to test whether named cognitive mechanisms in LLM-based agent systems are actually implemented and used. In the audited system claiming 25 cognitive mechanisms, the computed value never reached any production output in 18 modules; the authors identify three idle-wiring patterns: connected-but-never-called, called-but-inert, and called-but-content-blind. Across 61 frozen criteria in 12 interventions, 53 were confirmed, five falsified, two partially adjudicated, and one adjudication-ambiguous. Two repair experiments raised planner task success from 0.028 to 1.000 and cut state-controller error by 52%.

Why it matters: It gives agent developers a reusable audit method and concrete evidence that module names can substantially overstate implemented computation.

arxiv4d agoScore 82

LLM agentscognitive architectureAI evaluation

Research Editor selected

VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression

A new arXiv study examines whether vision-language-action policies refrain from acting when instructions are missing, meaningless, infeasible, or refer to the wrong task. Across six checkpoints in LIBERO-Goal simulation, an empty instruction still caused movement in every episode, and five checkpoints often completed a task the instruction had not specified. Three suppression approaches were tested: an instruction prefix failed across all six checkpoints, an action-chunk gate held the robot on non-task inputs in five of six, and fine-tuning prevented task completion on non-task inputs but did not reliably keep the robot still or handle infeasible instructions.

Why it matters: It provides concrete evidence that current VLA policies can start and execute tasks without valid instructions, an important safety issue for embodied AI deployment.

arxiv4d agoScore 82

vision-language-action modelsrobot safetyinstruction following

Research

KARU: Reinforcement Unlearning of LLM Knowledge Associations

Researchers propose Knowledge Association (KA) as the target of LLM unlearning, defined as the conditional predictive distribution linking a query context to its target knowledge. They introduce Knowledge Association Reinforcement Unlearning (KARU), a reinforcement learning framework that combines token-level reward construction, distribution-level regularization, and proximity-aware retain sampling. Experiments across multiple LLMs and benchmarks report improved forgetting effectiveness while maintaining comparable retention and general utility.

arxiv4d agoScore 72

LLM unlearningKnowledge associationsReinforcement learning

Research

DA-CoT Uses Attention-Map Entropy to Decide When Reasoning Segmentation Needs Chain-of-Thought

Researchers propose Difficulty-Aware Chain-of-Thought (DA-CoT), a training-free framework for reasoning segmentation with multimodal large language models. The method uses the entropy of attention maps as a difficulty signal, triggering CoT only when grounding is uncertain to avoid “overthinking.” A Bidirectional Rethinking mechanism lets the model reverse its reasoning decision after an initial failure. The authors report state-of-the-art results on Reasoning Segmentation benchmarks with improved inference efficiency.

arxiv4d agoScore 73

Reasoning SegmentationChain-of-ThoughtMultimodal Large Language Models

Research

GRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning

The paper identifies Correct-but-Visually-Misgrounded Reasoning (CVMR) in vision-language models, where models can reach correct final answers despite misperceiving visual information during intermediate reasoning. It proposes GRIT, a training framework that incorporates visual grounding into reinforcement learning with verifiable rewards (RLVR) to mitigate CVMR. The authors also introduce GRIT-Bench for systematic evaluation across text length and reasoning depth. Experiments report improved reasoning accuracy and grounding fidelity over existing RLVR methods.

arxiv4d agoScore 76

vision-language modelsreinforcement learningvisual grounding

Research

Risk-Sensitive On-Orbit Inspection Planning with Distributional RL

The paper presents an adaptive planning approach for multi-target on-orbit inspection missions in low Earth orbit using distributional reinforcement learning. A Quantile-Regression Deep Q-Network agent selects the next target by jointly considering expected return and return variance through a mean-variance criterion, while modeling stochastic target risk states and a limited mission-time budget. Experiments with five target satellites show that a risk-aversion weight trades mean return for lower return variance and a reduced probability of mission loss, and that learned policies approach an exactly computed efficient frontier.

arxiv4d agoScore 68

Distributional Reinforcement LearningOn-Orbit InspectionRisk-Sensitive Planning

Research

Activation Steering Causes Discourse-Level Shifts, and a Second Vector Can Offset Them

This paper shows that activation steering in LLMs systematically changes discourse dimensions such as formality, use of evidence, and ambiguity, beyond the intended behavior change. Prior work measured such side effects only through coarse signals like perplexity. The authors propose adding a second complementary steering vector aimed at the affected discourse dimension. They report restoring about 78% of unintended discourse changes while preserving the intended steering behavior in about 93% of generations.

arxiv4d agoScore 78

activation steeringLLM behavior controldiscourse-level effects

Research Editor selected

Breaking Information Islands in Sparse Tuning via Small-World Connectivity

The paper introduces SWIFT, a sparse tuning framework that combines block-diagonal sparse updates for local feature mixing with a fixed deterministic scattering permutation that routes mixed outputs across feature groups without extra trainable parameters. It argues that limited or uneven connectivity in a sparse update's structural support can create information islands that restrict task-specific feature interactions. Theoretical analysis shows that a sufficiently scattering permutation can guarantee positive block-level expansion under specified block conditions. Experiments on commonsense reasoning, natural language generation, and image classification report average gains over evaluated PEFT baselines and performance matching or surpassing full fine-tuning, with code released.

Why it matters: It offers a concrete, theoretically motivated PEFT method with released code and experiments across language and vision tasks, useful to researchers and practitioners working on efficient model adaptation.

arxiv4d agoScore 82

sparse fine-tuningparameter-efficient fine-tuninglarge language models

Research

OT-CAL uses optimal transport to adaptively select training samples and cut backprop cost

Researchers introduce OT-CAL, a data selection method that adjusts which samples receive backward passes as training progresses. Each sample is described by five informativeness features such as difficulty, learnability and uncertainty, and optimal-transport matching assigns selection weights against a curriculum vector that is updated from the selected samples, removing the need for a manual schedule. On three standard visual learning benchmarks, selecting 50% of each mini-batch improved results by 1.3-2.1 points over AdaSelection while training 1.18-1.24 times faster than full-data training.

arxiv4d agoScore 68

optimal transportdata selectiontraining efficiency

Research

TSBPO: Tree-Structured Sampling and Update for Training Interactive Language Agents

The paper introduces TSBPO, a tree-structured sampling-to-update training paradigm for interactive language agents trained from terminal outcomes. It combines a likelihood-fitted Root/Branch allocator that targets contrasting-outcome continuations, sibling-based trajectory advantages with observation-group step advantages, and complete-response token-loss aggregation. With a Qwen2.5-1.5B actor, the authors report 97.79% seen and 96.64% unseen stochastic success on ALFWorld, plus 81.07% per-episode success on WebShop over six stochastic episodes per test goal. An ablation and aggregation comparison are also reported.

arxiv4d agoScore 78

interactive language agentsreinforcement learningTSBPO