Singapore

We present ReCAD, a reinforcement learning framework that bootstraps pretrained large models (PLMs) to generate precise parametric computer-aided design (CAD) models from multimodal inputs, by leveraging their inherent generation capabilities. With just access to simple functional interfaces (e.g., point coordinates), our approach enables the emergence of complex CAD operations (e.g., pattern replication} and mirror). This stands in contrast to previous methods, which typically rely on knowledge injected through supervised fine-tuning (SFT), offer limited support for editability, and fail to fully exploit the strong generative priors of PLMs. Specifically, the ReCAD framework begins by fine-tuning vision-language models (VLMs) to equip them with basic CAD model generation capabilities, where we first rewrite hardcoded CAD scripts into parameterized code, which is then leveraged to generate accurate textual descriptions for supervision. Then, we propose a novel reinforcement learning strategy that incorporates parameterized code as guidance to enhance the model’s reasoning on challenging questions, and further employ a curriculum-based hierarchical primitive learning process to progressively teach structured and compositional skills under a unified reward function that ensures both geometric accuracy and semantic fidelity.
ReCAD establishes a new state-of-the-art on both text-to-CAD and image-to-CAD tasks, significantly improving geometric accuracy across in- and out-of-distribution settings. In the in-distribution setting, ReCAD reduces the mean Chamfer Distance (CD) from 73.47 to 29.61, and in the out-of-distribution setting, mean CD drops from 272.06 to 80.23, outperforming all existing baselines by a substantial margin.

AAAI 2026

ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models

large vision models

3d computer vision

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

technical paper

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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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.

With the rise of generative artificial intelligence
(GenAI), academic dishonesty in classrooms has skyrocketed,
yet the existing solutions for detecting such dishonesty
often fall short. Standard "AI detectors" merely analyze
one text at a time, failing to account for students'
previous writings, which risks erroneous predictions.
Meanwhile, existing token-based authorship verification
(AV) models fail to analyze the nuances in writing styles
that truly distinguish authorship. To fill this existing
gap, we propose a novel AV framework that combines
token-level stylometric features (e.g., POS tag patterns)
with handcrafted stylistic features (e.g., sentence
structure variation) to construct a comprehensive feature
set. Using both benchmark corpora and real-world high
school student essays, we trained multiple machine learning
classifiers using the proposed feature set. Our initial
experiments show that our approach outperforms the standard
token-only baselines by over 25%, while offering
interpretable, style-based insights. These preliminary
results highlight the importance of nuanced stylistic
features and suggest that a holistic AV system can provide
educators with more reliable and transparent detection
tools. Looking ahead, we plan to extend this work with
large language models and multi-agent approaches to further
enhance robustness and adaptability.

Style-First Authorship Verification for Academic Integrity in the Generative AI Era (Student Abstract)

Real-world AI systems are tackling increasingly complex
problems, often through interactions among Large Language
Model (LLM) agents. When these agents develop inconsistent
conventions, coordination can break down. Applications such
as collaborative coding and distributed planning therefore
require reliable, consistent communication, and scalability
is a central concern as systems grow. We introduce
Schema-Induced Games for Naming (SIGN), a naming game that
examines how lightweight structure can steer convention
formation. We compare schema-induced communication to
unconstrained natural language and find faster convergence
with up to 5.8× higher agreement. These results suggest
that minimal structure can act as a simple control knob for
efficient multi-agent coordination, pointing toward broader
applications beyond the naming game.

SIGN: Schema Induced Games for Naming (Student Abstract)

Automated news summarization using large language models
(LLMs) offers great potential to enhance information
accessibility. However, critical challenges, such as
hallucinations, bias, and toxicity, threaten their
reliability and societal acceptance. In this paper, we
present NewsLensAI, a novel summarization framework
explicitly designed to address these trustworthiness
concerns through Named Entity Recognition (NER)-guided
prompting. By anchoring summaries in key factual entities
extracted from source articles, our method significantly
reduces factual inaccuracies without altering model weights
or architectures. We evaluate NewsLensAI on a dataset of
1,500 real-world news articles using both open- source
(LLaMA3) and proprietary (Gemini1.5) LLMs. Our analysis
encompasses factual consistency, political bias shifts,
sentiment preservation, and toxicity moderation. Our
results indicate substantial improvements in factual align-
ment, demonstrated by an average BERTScore increase from
0.80 (baseline) to 0.88 (NER-enhanced), and a marked 70%
relative reduction in hallucinated entities. Furthermore,
we identify and characterize a notable “centrist drift,”
wherein summaries tend to moderate extreme biases present
in source articles, along with a measurable reduction in
toxic or emotionally charged language. Complementing our
empirical findings, we introduce a real-time NewsLensAI
demo that summarizes live news feeds from the Guardian API,
providing dynamic bias and sentiment analysis. This
practical implementation underscores the real-world
applicability and potential societal benefit of our
approach. Finally, we dis- cuss critical ethical
implications, including potential impacts on media literacy
and information diversity. Our interdisciplinary approach,
linking NLP, journalism, and ethical analysis, positions
NewsLensAI as a meaningful step toward safer, fairer and
more trustworthy AI-generated news consumption.

NewsLensAI: NER-Guided Summarization for Mitigating Hallucination and Bias in LLM-Based News Summaries (Student Abstract)

We present APEX-Q, a flexible product quantization framework for compressing large language models. Unlike prior multi-codebook quantization methods with fixed partitions, APEX-Q supports arbitrary-dimensional tensor quantization, better capturing weight redundancy. It achieves performance on par with 4-bit and 8-bit baselines, enables post-training quantization without retraining, and reveals key trade-offs across subvector dimensions, codebook sizes, and hardware efficiency. APEX-Q thus provides a unified, hardware-friendly approach to scalable LLM deployment.

APEX-Q: Arbitrary-dimension Product-EXtension Quantization for Accelerated LLM Deployment (Student Abstract)

As tropical cyclones intensify and track forecasts become increasingly uncertain, U.S. ports face heightened supply-chain risk under extreme weather conditions. Port operators need to rapidly synthesize diverse multimodal forecast products, such as probabilistic wind maps, track cones, and official advisories, into clear, actionable guidance as cyclones approach. Multimodal large language models (MLLMs) offer a powerful means to integrate these heterogeneous data sources alongside broader contextual knowledge, yet their accuracy and reliability in the specific context of port cyclone preparedness have not been rigorously evaluated. To fill this gap, we introduce CyPortQA, the first multimodal benchmark tailored to port operations under cyclone threat. CyPortQA assembles 2,917 real-world disruption scenarios from 2015 through 2023, spanning 145 U.S. principal ports and 90 named storms. Each scenario fuses multi-source data (i.e., tropical cyclone products, port operational impact records, and port condition bulletins) and is expanded through an automated pipeline into 117,178 structured question–answer pairs. Using this benchmark, we conduct extensive experiments on diverse MLLMs, including both open-source and proprietary model. MLLMs demonstrate great potential in situation understanding but still face considerable challenges in reasoning tasks, including potential impact estimation and decision reasoning.

CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation

Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities in both large language models (LLMs) and multimodal large language models (MLLMs). However, its reliability is often undermined by the accumulation of errors in intermediate steps. This paper proposes a novel approach to calibrating CoT reasoning accuracy by leveraging the model’s internal cognition of truthfulness. Our findings suggest that the model implicitly tracks the evolving veracity of intermediate steps throughout the dynamic, progressive reasoning process. We train a confidence predictor to quantify the model’s internal cognition of truthfulness at each reasoning step, enabling dynamic selection of the most plausible reasoning path through beam search. Experimental results demonstrate that our method significantly outperforms the state-of-the-art baselines (e.g., Self-Consistency, and PRM Guided Search) across the mathematical, symbolic, and commonsense reasoning tasks, exhibiting superior accuracy and reliability in both unimodal and multimodal settings. This study proposes a novel path toward improving the reliability of CoT reasoning, demonstrating strong potential for wide-ranging applications.

Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning

Offline policy learning from logged data is a critical paradigm for enabling effective decision-making without costly online exploration. However, its application has been largely confined to single-objective problems, a stark contrast to real-world scenarios where decision-making inherently involves navigating multiple, often conflicting, objectives. This paper introduces a comprehensive framework for Offline Multi-Objective Bandits (OffMOB), providing a principled solution to the fundamental challenge of learning Pareto-optimal policies from a static dataset. Our core contribution is a novel algorithm that uniquely integrates the pessimism principle with multi-objective optimization to safely learn from off-policy data. Crucially, our approach transcends the primary limitation of scalarization techniques, which are restricted to finding a single policy for a pre-defined preference. Instead, OffMOB directly approximates the entire Pareto front, learning a single, flexible policy model capable of generating an optimal action for any desired trade-off. To rigorously evaluate performance, we introduce the Tchebycheff sub-optimality metric and establish the first finite-sample generalization bounds for this problem class, proving that our algorithm converges to the true Pareto front under practical data coverage assumptions. Extensive experiments on complex benchmarks demonstrate that OffMOB significantly outperforms existing methods, identifying the complete set of optimal trade-offs where naive extensions and single-objective methods fail.

Offline Multi-Objective Bandits: From Logged Data to Pareto-Optimal Policies

Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs—such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering: as a structured, iterative code-generation process. AEC decomposes ZSEE into specialized subtasks—retrieval, planning, coding, and verification—each handled by a dedicated LLM agent. Event schemas are represented as executable class definitions, enabling deterministic validation and precise feedback via a verification agent. This programming-inspired approach allows for systematic disambiguation and schema enforcement through iterative refinement. By leveraging collaborative agent workflows, AEC enables LLMs to produce precise, complete, and schema-consistent extractions in zero-shot settings. Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines, showcasing the power of treating event extraction like code generation.

Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction

Modern ETL (Extract, Transform, Load) tools offer graphical, no-code interfaces for workflow creation but still require users to manually identify transformation functions and configure their properties, which is time-consuming and demands prior expertise. We present the research and engineering foundations of the IBM DataStage Assistant, a deployed capability that generates complete multi-stage ETL flows directly from natural language (NL) descriptions. Our framework infers transformation functions, their properties, and transformer expressions, enabling novices to discover relevant functions and allowing experts to bypass manual configuration. The proposed framework achieves a prediction accuracy of 96.4% for flow predictions, 87.0% for properties, and 83.6% for transformer expressions. We also show a document exploration module that uses retrieval-augmented generation (RAG) over product documentation to answer tool-specific questions in NL. Implemented in IBM DataStage, this approach supports iterative, in-environment workflow design and reduces context switching. In initial studies, it achieves up to 90% time savings for novices and 50% for experts.

From Natural Language to Executable ETL Flows: The IBM DataStage Assistant

Self-supervised learning (SSL) is an emerging paradigm that
exploits supervisory signals generated from the data
itself, and many recent studies have leveraged SSL to
conduct graph anomaly detection. However, we empirically
found that three important factors can substantially impact
detection performance across datasets: (1) the specific SSL
strategy employed; (2) the tuning of the strategy’s
hyperparameters; and (3) the allocation of combination
weights when using multiple strategies. Most SSL-based
graph anomaly detection methods circumvent these issues by
arbitrarily or selectively (i.e., guided by label
information) choosing SSL strategies, hyperparameter
settings, and combination weights. While an arbitrary
choice may lead to subpar performance, using label
information in an unsupervised setting is label information
leakage and leads to severe overestimation of a method’s
performance. Leakage has been criticized as “one of the top
ten data mining mistakes", yet many recent studies on
SSL-based graph anomaly detection have been using label
information to select hyperparameters. To mitigate this
issue, we propose to use an internal evaluation strategy
(with theoretical analysis) to select hyperparameters in
SSL for unsupervised anomaly detection. We perform
extensive experiments using 10 recent SSL-based graph
anomaly detection algorithms on various benchmark datasets,
demonstrating both the prior issues with hyperparameter
selection and the effectiveness of our proposed strategy.

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