Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, planning remains a bottleneck, as existing methods struggle with long-horizon strategies. Our analysis reveals a critical phenomenon—plan anchor—where the first reasoning step disproportionately impacts downstream behavior in long-horizon web reasoning tasks. Current RL algorithms, fail to account for this by uniformly distributing rewards across the trajectory.To address this, we propose Anchor-GRPO, a two-stage RL framework that decouples planning and execution. In Stage 1, the agent optimizes its first-step planning using fine-grained rubrics derived from self-play experiences and human calibration. In Stage 2, execution is aligned with the initial plan through sparse rewards, ensuring stable and efficient tool usage. We evaluate Anchor-GRPO on four benchmarks: BrowseComp, BrowseComp-Zh, GAIA, and XBench-DeepSearch. Across models from 3B to 30B, Anchor-GRPO outperforms baseline GRPO and First-step GRPO, improving task success and tool efficiency. Notably, WebAnchor-30B achieves 46.0% pass@1 on BrowseComp and 76.4% on GAIA. Anchor-GRPO also demonstrates strong scalability, getting higher accuracy as model size and context length increase.
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Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and peer review, remains largely underexplored. Existing approaches typically rely on off-the-shelf LLMs or simple pipelines, which struggle with long-context understanding and often fail to produce targeted and persuasive responses. In this paper, we propose **DRPG**, an agentic framework for automatic academic rebuttal generation that operates through four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan rebuttal strategies, and Generate responses accordingly. Notably, the Planner in DRPG reaches over 98% accuracy in identifying the most feasible rebuttal direction. Experiments on data from top-tier conferences demonstrate that DRPG significantly outperforms existing rebuttal pipelines and achieves performance beyond the average human level using only an 8B model. Our analysis further demonstrates the effectiveness of the planner design and its value in providing multi-perspective and explainable suggestions. We also showed that DRPG works well in a more complex multi-round setting. These results highlight the effectiveness of DRPG and its potential to provide high-quality rebuttal content and support the scaling of academic discussions.
Steering large language model (LLM) reasoning via high-level reasoning actions offers a promising approach to improve robustness and interpretability. However, existing action-based paradigms, ranging from training-free prompting to static plan retrieval or prediction, often fail to consistently outperform standard generation because their planners tend to degenerate into repetitive loops or fixed patterns. We propose PG-HAP (Policy-Guided High-Level Action Planning), a lightweight stepwise planner–executor framework that learns to select reasoning actions dynamically while keeping the executor LLM fully frozen. The planner is trained with reinforcement learning to optimize answer correctness. To prevent degeneration, we introduce two targeted mechanisms: (i) an Action-Dependency Logit Mask that enforces valid transitions to avoid redundancy, and (ii) an Action Diversity Reward that discourages mode collapse by promoting varied action sequences. Across mathematical and commonsense reasoning benchmarks, PG-HAP improves accuracy over strong baselines while producing less redundant, more adaptive trajectories. This demonstrates that learning high-level planning alone can substantially strengthen reasoning without expensive end-to-end model tuning.
NITI: Neural Plan Concretization for Incremental Execution, Bridging and Trigger Inference from Underspecified Human Policies
PDF ↗Human decision-making in safety-critical domains is governed by abstract policies that intentionally omit exhaustive preconditions/ triggers and contingencies. Executing such underspecified policies reliably in open-world settings remains a fundamental challenge for large language models (LLMs). We introduce NITI, Neural Bridging for Incremental Execution and Trigger Inference from Underspecified Human Policies, a neuro-symbolic framework that treats LLMs not as autonomous planners, but as execution-time concretizers of human intent. NITI incrementally executes abstract policies via verifier-grounded interfaces, infers implicit applicability conditions, repairs execution through neural bridging when assumptions fail, and halts safely under state inconsistency. We evaluate NITI on two structurally distinct embodied domains: a new benchmark of World Cubing Championship 2×2 Rubik’s Cube scrambles (n=50) and a safety-critical automated insulin dosing task. Across multiple frontier LLMs, NITI enables reliable long-horizon execution without task-specific training or search with minimal contextualization infence overhead, substantially outperforming one-shot and chain-of-thought baselines. Our results show that compositional, verifier-grounded execution is essential for safe human–AI collaboration in open-world decision-making. Code and benchmark available here - https://github.com/ImpactLabASU/ACLNITI
Recent work, such as TripCraft and TravelPlanner, has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation. However, real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attractions. To address this gap, we introduce **TripTide**, the first benchmark designed to evaluate the ability of LLMs to revise travel itineraries under realistic disruptions.TripTide models both disruption severity and traveler tolerance, enabling systematic evaluation of how LLMs respond to unexpected travel events. The benchmark simulates scenarios where existing itineraries must be revised while preserving the traveler’s original intent and respecting practical constraints. We conduct a three-fold evaluation of itinerary revision quality: (i) Automatic metrics measuring *Preservation of Intent*, *Responsiveness*, and *Adaptability* (semantic, spatial, and sequential), (ii) LLM-as-a-Judge evaluation assessing the quality and plausibility of revised itineraries and (iii) Human evaluation examining overall revision quality and user satisfaction.Our findings show that LLMs generally preserve semantic intent and sequential structure, while spatial deviations are more pronounced in shorter itineraries and diminish for longer ones. However, the ability to handle disruptions degrades as itinerary length increases, highlighting limitations in long-horizon itinerary revision. The TripTide benchmark provides a foundation for systematically evaluating robustness and adaptability in LLM-based travel planning systems.
In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce \textbf{AutoPage}, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated "Checker" agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author’s vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct \textbf{PageBench}, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than $0.1. Code and data will be released.
The Model Context Protocol (MCP) aims to standardize the integration of Large Language Models (LLMs) with external tools, yet existing research primarily evaluates functional capabilities while treating the underlying protocol as an opaque black box. This oversight obscures critical inefficiencies in token flows and latency distributed across MCP’s decoupled Host-Client-Server architecture. In this paper, we introduce ProMCP, an end-to-end profiling and instrumentation framework that decomposes the MCP workflow into a six-stage communication pipeline, enabling granular attribution of computational costs. We evaluate widely varying deployment topologies—from air-gapped local models to commercial off-the-shelf (OTS) clients—across 20 servers and 169 tools from MCP-Bench and MCP-Universe. Our analysis reveals a distinct inversion in performance bottlenecks: topologies with customized clients devote 56–72% of total tokens and 60–67% of latency to planning and schema injection, whereas OTS clients concentrate over 85% of latency in final answer synthesis. Crucially, actual tool execution constitutes a negligible fraction of the total cost across all configurations. These findings establish a quantitative baseline for protocol overhead and demonstrate that future optimization must target schema orchestration and transport efficiency rather than tool execution speed. The code is available at: https://github.com/ResponsibleAILab/ProMCP.
Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing
PDF ↗Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and undesired outputs poses risks. This unwanted behavior is further exacerbated in environments where sensors are noisy or unreliable. Characterizing the behavior of LLM planners to varied observations is necessary to proactively avoid failures in safety-critical scenarios. We specifically investigate the response of LLMs along two different perturbation dimensions. Like prior works, one dimension generates semantically similar prompts with varied phrasing by randomizing order of details, modifying access to few-shot examples, etc. Unique to our work, the second dimension simulates access to varied sensors and noise to mimic raw sensor or detection algorithm failures. An initial case study in which perturbations are manually applied show that both dimensions lead LLMs to hallucinate in a multi-agent driving environment. However, manually covering the entire perturbation space for several scenarios is infeasible. As such, we propose a novel method for efficiently searching the space of prompt perturbations using adaptive stress testing (AST) with Monte-Carlo tree search (MCTS). Our AST formulation enables discovery of scenarios, sensor configurations, and prompt phrasing that cause language models to act with high uncertainty or even crash. By generating MCTS prompt perturbation trees across diverse scenarios, we show through extensive experiments that offline analyses can be used to proactively understand potential failures that may arise at runtime. Code is available at https://sites.google.com/illinois.edu/astllm/.
Automated Program Repair (APR) is vital for software maintenance. Despite notable advancements, existing methods still face challenges of insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. We propose CascadeFix, a multi-location automatic repair method via cascading planning and generation. Firstly, to improve the modeling of semantic and structural dependencies among bugs, three types of bug relationships-Use, Copy, and Nearby-are defined to characterize semantic connection, patch reusability, and contextual interference. Then, to address inadequate global repair planning, a cascading repair planning algorithm is designed to effectively cluster strongly correlated bugs and intelligently assign reasonable repair priorities and operations to each cluster, ensuring the rationality and consistency of global repair. Finally, taking clusters as the basic repair units, a cascading patch generation mechanism is proposed to dynamically integrate intra-cluster dependency information and cross-cluster repair knowledge, producing patches that maintain syntactic correctness and semantic consistency under global dependency constraints. Experiments on Defects4J show that CascadeFix resolves 84 multi-location bugs, achieving a 31% improvement over current state-of-the-art methods.
Agent for Numerical Data Retrieval and Understanding by Code Generation and Multimodal Reasoning
PDF ↗Numerical data from sensors and time series are widely used in scientific research fields such as nuclear fusion experiments, which generate vast amounts of complex, high-dimensional data. Therefore, efficient numerical data analysis tools are crucial to accelerate experimental research. Large language models (LLMs) have emerged as promising solutions to analyze numerical data with natural language queries. However, LLMs have difficulties treating this type of data as they have been designed for text in the first place. To overcome these limitations, we propose a model-agnostic and data-agnostic agent that processes numerical data by code generation and multimodal reasoning. Our agent demonstrates competitive performance against baselines on benchmark data on numerical data tasks such as sensor data classification and time series understanding. While outperforming them on information retrieval benchmarks, also we have successfully applied our agent in the context of nuclear fusion research, where physicists and Tokamak operators interact with it to plan and analyze fusion experiments.
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
PDF ↗Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at understanding single plots, they often struggle with multi-step reasoning across multiple subplots. We propose HierVA, a hierarchical visual agent framework for chart reasoning that iteratively constructs and updates a working context in a joint image–text space. A high-level manager generates plans and maintains a compact context containing only key information, while specialized sub-agents perform reasoning, gather evidence, and return results. In particular, the agent maintains separate visual and textual contexts, using a zoom-in tool to restrict the visual context. Experiments on the chart reasoning benchmarks demonstrate consistent improvements over strong multimodal baselines, and ablation studies verify that hierarchical architecture, limited visual context, and distilled context contribute complementary gains.
From Scaffolding to Assimilation: Progressive Structural Internalization for Format-Constrained Creative Text Generation
PDF ↗While Large Language Models (LLMs) demonstrate remarkable capabilities in open-ended creative generation, they notably struggle with Format-Constrained Generation tasks—such as poetry and lyrics—where strict adherence to multidimensional structural constraints (i.e., format, phonetics, and rhyme) is prerequisite to aesthetic value. Existing paradigms predominantly rely on unreliable prompting or rigid constrained decoding strategies; the former often fails to ensure compliance, while the latter compromises inference latency and disrupts the natural probability distribution, degrading generation quality. To bridge this gap, we establish CCP-Arena, a rigorous testbed for Chinese Classical Poetry, and proposeProgressive Structural Internalization (PSI) a novel framework designed to embed external constraints into the model’s intrinsic intuition. PSI initiates withStructural Scaffolding via Explicit Cognitive Planning, utilizing explicit template to provide a structural scaffold for subsequent generation. This is followed by a Cascaded Reinforcement Learning stage guided by a Holistic Reward Model, which optimizes for precise structural-semantic alignment. Extensive experiments demonstrate that PSI achieves state-of-the-art performance, surpassing baselines in both strict constraint adherence and literary aesthetics. Furthermore, mechanistic analysis confirms that our method effectively internalizes structural information into the model’s latent representations, offering a robust and efficient solution for constrained creative generation.
While LLMs demonstrate remarkable fluency in narrative generation, existing methods struggle to maintain global narrative coherence, contextual logical consistency, and smooth character development, often producing monotonous scripts with structural fractures. To this end, we introduce PLOTTER, a framework that performs narrative planning on structural graph representations instead of direct sequential text representations in existing work. Specifically, PLOTTER executes the Evaluate-Plan-Revise cycle on the event graph and character graph. By diagnosing and repairing issues of the graph topology under rigorous logical constraints, the model optimizes the causality and narrative skeleton before complete context generation. Experiments demonstrate that PLOTTER significantly outperforms representative baselines across diverse narrative scenarios. These findings verify that manipulating narrative planning on structural graph representations—rather than direct text representations—is crucial to enhance the long-context reasoning of LLMs in complex narrative generation.
Multimodal Large Language Models (MLLMs) are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insufficient. We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real-world kitchen hazards. While existing safety evaluations focus on hazard recognition through disembodied question answering (QA) settings, we evaluate eleven state-of-the-art models from the Qwen, Gemma, and Gemini families on not only hazard recognition, but also active risk mitigation through embodied task planning. Our experimental results reveal a significant alignment gap: while models can accurately recognize hazards in QA settings, average mitigation success rates for these hazards are low in comparison. Our findings demonstrate that static evaluations through QA are insufficient for physical safety, advocating for a paradigm shift toward benchmarks that prioritize multi-step corrective actions in embodied context.
Recent text-to-video (T2V) diffusion models have made remarkable progress in generating high-quality videos. However, they often struggle to align with complex text prompts, particularly when multiple objects, attributes, or spatial relations are specified. We introduce VideoRepair, the first self-correcting, training-free, and model-agnostic video refinement framework that automatically detects fine- grained text–video misalignments and performs targeted, localized corrections. Our key insight is that even misaligned videos usually contain correctly generated regions that should be preserved rather than regenerated. Building on this observation, VideoRepair proposes a novel region-preserving refinement strategy with three stages: (i) misalignment detection, where MLLM-based evaluation with automatically generated evaluation questions identifies misaligned regions; (ii) refinement planning, which preserves correctly generated entities, segments their regions across frames, and constructs targeted prompts for misaligned areas; and (iii) localized refinement, which selectively regenerates problematic regions while preserving faithful content through joint optimization of preserved and newly generated areas. On two benchmarks, EvalCrafter and T2V-CompBench with four recent T2V backbones, VideoRepair achieves substantial improvements over recent baselines across diverse alignment metrics. Comprehensive ablations further demonstrate the efficiency, robustness, and interpretability of our framework.
Recent advances in autonomous digital agents from industry (e.g., Manus AI and Gemini’s research mode) highlight their potential for structured tasks through autonomous decision-making and task decomposition, but it remains unclear how well such systems support real-world information-intensive workflows. We study this question in journalism, where newswriting requires iterative planning, contextual reasoning, and active discovery of missing background to produce a coherent article. We introduce NEWSAGENT, a benchmark for evaluating how agents search raw materials, select relevant information, and iteratively revise drafts through core journalistic functions. Given a writing instruction and partial firsthand materials, agents must identify narrative perspectives, issue keyword-based queries, retrieve historical context, and generate complete news articles. Unlike typical summarization or retrieval tasks, essential context is not directly available and must be actively discovered, reflecting real-world reporting constraints. NEWSAGENT consists of 6k human-verified examples derived from real news. We evaluate open- and closed-sourced LLMs with commonly-used agentic frameworks on NEWSAGENT, which shows that agents are capable of retrieving relevant facts but struggling with planning and narrative integration. We believe that NEWSAGENT serves a realistic testbed for iterating and evaluating agent capabilities in terms of web data manipulation to real-world productivity. The benchmark resources are publicly available at https://github.com/wywyWang/CoachAI-Projects.
Writing a literature review requires a deep understanding of the relationships among cited papers: how they build on, challenge, or offer alternative perspectives to one another. We present Graph-Reasoning Aided Survey Planning (GRASP), a framework combining LLM planning for related work generation with graph algorithms to extract key relationships among cited papers. Our two-layer graph structure consists of a Graph of Thoughts and an Argument-Counteragument Planning Network, representing the cited papers at different levels of granularity, and we apply topology-aware pruning via a Steiner tree to identify the core inter-paper relationships captured in our graph. Our citation analysis-based evaluation shows that GRASP generates RWS that closely match human-written targets in terms of the discourse roles, intents, and grouping of citations.
RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit "I DON’T KNOW" even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.
GAVEL: Evidence-Contract Debate with Mechanized Scrutiny for Provenance-Grounded Fact-Checking
PDF ↗Evidence-grounded fact-checking requires predicting claim veracity while returning faithful evidence at fine granularity, including exact sentences, table cells, and complete multi-document chains. Although large language models enable decomposition, planning, and multi-agent verification, they can still produce convincing rationales with weak provenance, especially under heterogeneous evidence and multi-hop requirements. We propose GAVEL, a multi-agent debate framework that enforces evidence grounding throughout inference. GAVEL introduces an Evidence Contract that requires debaters to state atomic subclaims and bind each to explicit evidence units, and a Mechanized Chain of Scrutiny in which a neutral Scrutinizer audits outputs and performs deterministic validation of cited identifiers and quoted spans. A Judge then selects a sufficient evidence set and produces the final decision. Experiments on FEVEROUS and HOVER in an open-book setting show that GAVEL improves provenance-aware metrics that jointly require correct labels and correct, complete evidence over strong recent baselines. Ablations confirm that both evidence binding and mechanized citation validation are key to the gains.
EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents
PDF ↗Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialogue corpora are largely dyadic or lack the multi-party workflow and annotations needed for this setting. We introduce an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. The pipeline yields EMSDialog, a dataset of 4,414 synthetic multi-speaker EMS conversations based on a real-world ePCR dataset, annotated with 43 diagnoses, speaker roles, and turn-level topics. Human and LLM evaluations confirm high quality and realism of EMSDialog using both utterance- and conversation-level metrics. Results show that EMSDialog-augmented training improves accuracy, timeliness, and stability of EMS conversational diagnosis prediction. Our datasets and code are publicly available at https://uva-dsa.github.io/EMSDialog