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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.

ACL 2026

SafetyALFRED: Evaluating Safety-Conscious Planning of Vision Language Models

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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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.

AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principled approach to address this challenge. We analyze the needs and potential benefits of interpretable automated scoring for various assessment stakeholder groups and develop four principles of interpretability -- (F)aithfulness, (G)roundedness, (T)raceability, and (I)nterchangeability (FGTI) -- targeted at those needs. To illustrate the feasibility of implementing these principles, we develop the AnalyticScore framework as a baseline reference framework. When applied to the domain of text-based constructed-response scoring, AnalyticScore outperforms many uninterpretable scoring methods in terms of scoring accuracy and is, on average, within 0.06 QWK of the uninterpretable SOTA across 10 items from the ASAP-SAS dataset. By comparing against human annotators conducting the same featurization task, we further demonstrate that the featurization behavior of AnalyticScore aligns well with that of humans.

Interpretability from the Ground Up: Stakeholder-Centric Design of Automated Scoring in Educational Assessments

LLM-based evaluation systems (LLM judges) have emerged as a scalable alternative to expensive human evaluations. Although LLM judges demonstrate 70-80% agreement with human evaluators, their robustness under semantically equivalent prompt variations remains underexplored. Through systematic evaluation of 8 models across 4 NLG tasks using 10 semantically equivalent paraphrases per prompt (~115000 evaluations), we identify a critical accuracy-robustness gap: attribute verifiability affects the robustness more than model choice, with factually verifiable attributes achieving 0.71 accuracy versus 0.19 for subjective attributes. Our investigations discover three key insights: 1) Task structure characteristics influence the robustness and in turn accuracy, 2) Attribute verifiability as the strongest predictor-factually verifiable attribute achieve 0.71 accuracy versus 0.19 for subjective attributes, 3) No single winning model-smallest model (Llama-3.1-8B) exhibits second-best performance, while the strongest model (Llama-4) from the same family significantly lag behind, thus demonstrating that general capability improvements do not necessarily result in evaluation robustness. With these findings, we propose a diagnostic framework grounded in attribute verifiability that enables principled decisions about evaluation automation. Our work establishes new standards for assessing LLM judge reliability beyond simple accuracy metrics.

All Prompts Are Created Equal? Evaluating Robustness of LLM Judges Against Non-Adversarial Prompt Variations

Large language models (LLMs) are highly capable of answering questions, but they are often unaware of their own knowledge boundary, i.e., knowing what they know and what they don't know. As a result, they can generate factually incorrect responses on topics they do not have enough knowledge of, commonly known as hallucination. Rather than hallucinating, a language model should be more honest and respond with "I don't know" when it does not have enough knowledge about a topic. Many methods have been proposed to improve LLM honesty, but their evaluations lack robustness, as they do not take into account the knowledge that the LLM has ingested during its pretraining. In this paper, we propose a more robust evaluation benchmark dataset for LLM honesty by utilizing Pythia, a truly open LLM with publicly available pretraining data. In addition, we also propose a novel method for harnessing the pretraining data to build a more honest LLM.

 Parametric Knowledge is Not All You Need: Toward Honest Large Language Models via Retrieval of Pretraining Data

Short-form news videos increasingly shape public perception through strategic framing, yet existing verification methods largely overlook the communicative intent underlying such content. By emphasizing surface semantics, current models struggle to separate stylistic presentation from factual evidence, which leads to shortcut learning and brittle generalization. To address this limitation, we propose the Origin–Objective–Means (OOM) framework, a theory-grounded representation of communicative intent that captures creator stance, audience need activation, and communication strategy. We validate OOM through large-scale human annotation, revealing distinct and consistent lexical and structural patterns across intent dimensions. Building on this representation, we operationalize intent as an explicit semantic condition rather than a prediction target. Concretely, we introduce Intent-Guided Prompting (IGP) to condition LLM reasoning and intent-conditioned multimodal detection framework (ICMD), which injects intent into multimodal detectors via feature-wise modulation. Experiments on FakeSV and FakeTT show that modeling intent as an intermediate condition consistently improves accuracy and robustness across diverse vision–language backbones, while substantially reducing reliance on spurious stylistic correlations.

From What Is Said to Why It Is Framed: Intent-Aware News Video Understanding

Curiosity serves as a fundamental construct in human cognition.
Inspired by curiosity, reinforcement learning with intrinsic rewards for large language models (LLMs) has shown substantial potential.
However, it remains unclear whether existing curiosity-driven methods genuinely reflect curiosity-like behaviors in LLMs, and to what extent psychological notions of curiosity can be transferred to these models. In this work, we propose a psychology-inspired framework to evaluate and leverage curiosity in LLMs.
We adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs and combine questionnaire-based self reports with behavioral study.
We find that although LLMs can exhibit curiosity-like behavioral patterns resembling those of humans, such patterns do not reflect an intrinsic trait of curiosity.
Building on this insight, we design a curiosity-driven thinking pipeline to examine the functional role of human-like curious behaviors. Experiments show that instructing LLMs to emulate curious strategies leads to better performance on selected downstream tasks, indicating that mimicking curious behaviors holds promise for reasoning enhancement.

Why Did Apple Fall: Evaluating Curiosity in Large Language Models

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.

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

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.

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Interpretability from the Ground Up: Stakeholder-Centric Design of Automated Scoring in Educational Assessments

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