United States

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.

ACL 2026

ProMCP: Profiling Token Flows and Latency Costs in Model Context Protocol–Based LLM Agents

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NAML 2026

The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale. Code is in Software part under submission page.

 Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs

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.

TripTide: A Benchmark for Adaptive Travel Planning under Disruptions

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 - \texttt{https://github.com/ImpactLabASU/ACLNITI}

NITI: Neural Plan Concretization for Incremental Execution, Bridging and Trigger Inference from Underspecified Human Policies

The high cost of obtaining high-quality annotated data for in-context learning (ICL) has motivated the development of methods that use self-generated annotations in place of ground truth labels. While these approaches have shown promising results in few-shot settings, they generally do not scale to many-shot scenarios. In this work, we study ICL with self-generated examples using a framework analogous to traditional semi-supervised learning, consisting of annotation generation, demonstration selection, and in-context inference. Within this framework, we propose a simple baseline that outperforms ground truth ICL under zero-shot, few-shot, and many-shot settings. Notably, we observe consistent scaling behaviors with respect to the number of self-annotated demonstrations. To further extract performance from this many-shot capability, we introduce IterPSD, an iterative self-annotation approach that integrates iterative refinement and curriculum pseudo-labeling techniques from semi-supervised learning, yielding up to 6.8% additional gains on classification tasks. Motivated by our baseline and IterPSD results, we demonstrate that semi-supervised ICL offers a promising avenue for future ICL research.

 Many-Shot Scaling of In-Context Learning with Self-Generated Demonstrations

We assess LLMs' constitutional reasoning abilities using three different, newly developed datasets on three different constitutional questions in three different constitutional frameworks, comprising two different languages; the structure and content of the datasets is informed by legal expertise and grounded in the state of the art in philosophy of language. Our results indicate that the 19 LLMs tested, including the reasoning LLMs, while not being uniformly subject to political bias, are still not reliable constitutional reasoners, as they are heavily influenced by logically irrelevant aspects of the reasoning. Of the 196k evaluations run in our main experiment, the LLMs label less than 70\% correctly, and open-weight reasoning LLMs as well as gpt-4o are outperformed by moderately sized open-weight non-reasoning LLMs. None of the LLMs tested consistently show slow, systematic, rule-based system 2 thinking.

Too Fast, Too Shallow -- LLMs, Including Reasoning LLMs, Are Unreliable Constitutional Reasoners

Large language models (LLMs) are increasingly used as judges to evaluate, rank, and supervise other models, yet their reliability in judging LLMs' reasoning process under long-context settings remains underexplored. Existing benchmarks either overly rely on human annotators, who may miss subtle flaws in lengthy reasoning chains, or focus solely on final responses while ignoring the underlying context and reasoning process. We introduce Long-Reason Bench (LRBench), a large-scale benchmark for evaluating LLM-based judges. LRBench comprises over 100K annotated instances spanning medical, legal, and academic-review scenarios, with fine-grained labels indicating violations of six core principles: Logical Correctness, Factual Consistency, Bias and Fairness, Groundedness, Helpfulness, and Harmlessness. Experimental results reveal that state-of-the-art LLM judges struggle to identify nuanced reasoning errors in long contexts. To improve judge reliability, we further present Judge-R1, which combines reinforcement learning with multi-turn search to enable grounded and principle-aware evaluation. Across domains and principles, Judge-R1 consistently outperforms single-turn baselines, enabling scalable and trustworthy evaluation of LLM reasoning. Our dataset and code are available at https://github.com/Xinyi-0724/Judge-R1.

LRBench and Judge-R1: Principled Evaluation and Training of LLM-Based Judges for Long-Context Reasoning

Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and inference-time ones typically require access to the reward model at each inference step. We introduce **MEAV**, an inference-time model-editing-based LLM alignment method that learns encoded representations of preference dimensions, called *Alignment Vectors* (AV). These representations enable dynamic adjusting of the model behavior during inference through simple linear operations. Here, we focus on three gradual response levels across three specialized domains: medical, legal, and financial, exemplifying its practical potential. This new alignment paradigm introduces adjustable preference knobs during inference, allowing users to tailor their LLM outputs while reducing the inference cost by half compared to the prompt engineering approach. Additionally, we find that AVs are transferable across different fine-tuning stages of the same model, demonstrating their flexibility. AVs also facilitate multidomain, diverse preference alignment, making the process 12x faster than the retraining approach.

MEAV: Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrum

Automatic evaluation of book-length stories remains underexplored, particularly for non-English literature. 
We introduce ChangJuan, the first benchmark for *book-length Chinese story evaluation*, comprising 300 novels with metadata, human ratings, and large-scale user reviews. To mitigate the subjectivity of raw reviews, we propose a distillation method to aggregate them into generally agreed viewpoints (pros and cons) across key evaluation aspects such as plot and character. We conduct systematic experiments to benchmark current LLMs, analyze aspect importance, and examine genre differences. For book-length story evaluation, we propose an enhanced summary-based method that leverages length-detail balanced summaries and representative excerpts, generates aspect-specific reviews, and considers genre-aware aspect weighting to assign a final score. Using this framework and our distilled viewpoints, we fine-tune an 8B model, CLEM, which outperforms open-source baselines and raises Qwen3's Kendall's tau correlation with human judgments from 24.8 to 34.1. Our datasets and codes are available at https://github.com/DingyiYang/ChangJuan.

ChangJuan: A Comprehensive Benchmark for Book-Length Chinese Story Evaluation

Large language models (LLMs) have demonstrated strong performance in translation tasks. Subtitle translation presents unique challenges, such as preserving the original work’s worldview and the distinctive speaking styles of its characters. Achieving high-quality translations that reflect these stylistic nuances typically requires bilingual data for a specific movie, which is often scarce or unavailable. Thus, we propose a data augmentation method that uses LLMs to improve translation performance for specific movies, even when only a few hundred bilingual sentence pairs are available. The method expands source-side data by rewriting original subtitles using information that can be extracted from the context, such as character profiles and scene descriptions, to maintain the tone and thematic consistency of the movie. For translation, the augmented sentences are aligned with manually translated originals using structural similarity, which enables style-preserving bilingual data generation via one-shot learning. Experimental results show that data augmented using the proposed method effectively improves BLEU scores for film subtitle translation, and achieves superior stylistic quality in human evaluation.

Context-Driven and Reference-Guided Data Augmentation for Subtitle Translation

Do vision-language models (VLMs) develop more human-like sensitivity to linguistic concreteness than text-only large language models (LLMs) when both are evaluated with text-only prompts? We study this question with a controlled comparison between matched Llama text backbones and their Llama Vision counterparts across multiple model scales, treating multimodal pretraining as an ablation on perceptual grounding rather than access to images at inference. We measure concreteness effects at three complementary levels: (i) output behavior, by relating question-level concreteness to QA accuracy; (ii) embedding geometry, by testing whether representations organize along a concreteness axis; and (iii) attention dynamics, by quantifying context reliance via attention-entropy measures. In addition, we elicit token-level concreteness ratings from models and evaluate alignment to human norm distributions, testing whether multimodal training yields more human-consistent judgments. Across benchmarks and scales, VLMs show larger gains on more concrete inputs, exhibit clearer concreteness-structured representations, produce ratings that better match human norms, and display systematically different attention patterns consistent with increased grounding.

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