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PseudoSeer is a novel search engine for academic pseudocode, enabling retrieval over 320,000 algorithm implementations extracted from the arXiv. Using the system&#39;s caption-reference pairs, we study asymmetric retrieval, matching short queries with a median length of five words against long documents of roughly 300 words composed primarily of natural language with limited LaTeX notation. Our evaluation reveals scaling limitations in embedding models: a 149M parameter encoder outperforms 1.5B parameter alternatives, while BM25 remains competitive with pretrained models. Analyzing attention patterns over 33,000 caption document pairs, we identify two factors driving these results: attention efficiency and attention concentration. Models that significantly attend to sinks or non-discriminative tokens leave less attention for discriminative content, while models with overly diffuse attention fail to form discriminative representations. Guided by these findings, PseudoSeer’s embedding model, trained via contrastive learning with efficient attention patterns, outperforms the best pretrained model by 8.7 points. A hybrid approach combining learned embeddings with BM25 reaches 66.5\% R@10. PseudoSeer is deployed at pseudoseer.ist.psu.edu as both a practical search system and a benchmark for retrieval evaluation.

ACL 2026

PseudoSeer: a Search Engine for Pseudocode

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
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[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

PseudoSeer is a novel search engine for academic pseudocode, enabling retrieval over 320,000 algorithm implementations extracted from the arXiv. Using the system's caption-reference pairs, we study asymmetric retrieval, matching short queries with a median length of five words against long documents of roughly 300 words composed primarily of natural language with limited LaTeX notation. Our evaluation reveals scaling limitations in embedding models: a 149M parameter encoder outperforms 1.5B parameter alternatives, while BM25 remains competitive with pretrained models. Analyzing attention patterns over 33,000 caption document pairs, we identify two factors driving these results: attention efficiency and attention concentration. Models that significantly attend to sinks or non-discriminative tokens leave less attention for discriminative content, while models with overly diffuse attention fail to form discriminative representations. Guided by these findings, PseudoSeer’s embedding model, trained via contrastive learning with efficient attention patterns, outperforms the best pretrained model by 8.7 points. A hybrid approach combining learned embeddings with BM25 reaches 66.5\% R@10. PseudoSeer is deployed at pseudoseer.ist.psu.edu as both a practical search system and a benchmark for retrieval evaluation.

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.

Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker's final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, attacker–target placements, and base models. Results are consistent: denser connectivity, shorter attacker–target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker–target separation, and restrict hub/shortcut pathways via topology-aware access control. Our code is available at https://github.com/llll121/mama-eval.

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

Approximately 30% of autistic individuals remain non- or minimally-speaking throughout their lives, yet communicate richly through gestures, vocalizations, facial expressions, and augmentative devices. Interpreting this communication is an inherently multimodal task: caregivers rely on the simultaneous integration of visual cues, auditory signals, and contextual understanding to infer intent. Despite this natural alignment with multimodal large language models (MLLMs), research in this intersection remains narrowly focused on diagnosis rather than communication understanding. We address this gap by reframing the problem around two complementary dimensions: communicative actions (the physical modality) and communicative functions (the pragmatic intent). We analyze the ROSCO dataset, containing 2,903 caregiver-annotated video samples from 27 non- and minimally-speaking individuals, with multi-label annotations capturing up to three concurrent actions and two functions per sample across 6 action and 6 function classes. We further propose ROSCO-Omni, a teacher-student distillation framework that generates label-guided instruction data from a high-capability teacher MLLM and uses it to finetune a student MLLM for domain-specialized inference. ROSCO-Omni achieves performance comparable to closed-source models, demonstrating that open-source MLLMs can be adapted to understand communication in this underserved population.

ROSCO-Omni: Multimodal LLM-Based Communication Understanding for Non- and Minimally-Speaking Autistic Individuals

We introduce ChemBench, a comprehensive benchmark for evaluating LLMs' capabilities in analytical chemistry scenarios. Unlike existing benchmarks focused on factual knowledge, ChemBench assesses model abilities to provide contextualized, practical guidance for complex analytical chemistry challenges, including instrument readiness checks, system suitability testing, method development, and troubleshooting for both liquid chromatography coupled mass spectrometry (LC-MS) and Gas Chromatography-Mass Spectrometry (GC-MS) platforms. We evaluate three enhancement approaches: chemistry-specialized models, human-guided Chain-of-Thought reasoning, and Retrieval-Augmented Generation (RAG). Our findings reveal that general-purpose commercial models often outperform domain-specialized ones, while RAG and reasoning significantly improve performance. The six-dimension evaluation framework (specificity, correctness, usefulness, feasibility, misinformation risk, and error handling) provides valuable insights into LLMs' real-world utility for chemistry researchers, establishing a foundation for developing more effective AI assistants for scientific research.

LLMs as Lab Engineers: A Benchmark for Analytical Method Lifecycle Management

Weight tying, i.e. sharing parameters between input and output embedding matrices, is common practice in language model design, yet its impact on the learned embedding space remains poorly understood. In this paper, we show that tied embedding matrices align more closely with output (unembedding) matrices than with input embeddings of comparable untied models, indicating that the shared matrix is shaped primarily for output prediction rather than input representation. This unembedding bias arises because output gradients dominate early in training. Using tuned lens analysis, we show this negatively affects early-layer computations, which contribute less effectively to the residual stream. Scaling input gradients during training reduces this bias, providing causal evidence for the role of gradient imbalance. This is mechanistic evidence that weight tying optimizes the embedding matrix for output prediction, compromising its role in input representation. These results help explain why weight tying can harm performance at scale and have implications for training smaller LLMs, where the embedding matrix contributes substantially to total parameter count.

Weight Tying Biases Token Embeddings Towards the Output Space

Existing low-resource in-context learning-based knowledge graph question answering (KGQA) methods rely heavily on large language models (LLMs) to convert the natural language question into its corresponding logical form (LF), such as SPARQL, KoPL, etc. Recently, a few alignment techniques have been introduced that enable instruction-based fine-tuning of language models. They provide explicit negative signals and comparative objectives to learn how to avoid negative signals using preference optimization methods. Exploring such fine-tuning techniques with LLMs becomes very challenging due to the high computational resource requirements associated with them. Due to this, the focus has been shifted towards Small Language Models (SLMs), which offer advantages such as ease of (i) deployment for practical applications and (ii) instruction fine-tuning for specialized tasks. Motivated by this, in this work, we propose PO-KGQA: An SLM-based preference optimization framework for the complex KGQA task in a low-resource setting. Our extensive experiments demonstrate how PO-KGQA outperforms other fine-tuning alignment techniques on complex benchmarks such as KQA Pro by approximately 9% (avg).

PO-KGQA: Preference Optimization for Low-Resource Complex Knowledge Graph Question Answering

In simultaneous interpretation (SI), interpreters perform real-time translation by segmenting the source speech into chunks and translating them in the order they appear.
Since surface-matching metrics such as BLEU correlate poorly with human evaluations, translation quality is often evaluated using neural metrics that measure semantic similarity, such as COMET.
However, while SI translation ideally exhibits high monotonicity, COMET tends to assign higher scores to offline translations with long-distance reordering, because it is trained on such offline translation data.
To address this gap, we propose Simul-COMET, a variation of COMET adapted for SI evaluation specifically designed for monotonicity.
We train Simul-COMET on the SI-style translation data, which was converted from the offline translation of the COMET training data by leveraging large language models.
In English--Japanese translation experiments, we demonstrate that Simul-COMET assigns higher scores to SI-style translations than to offline ones.
Moreover, Simul-COMET shows stronger alignment with evaluation scores provided by professional interpreters than the original COMET.
Simul-COMET is available at https://github.com/kosuked/simul-comet.

Simul-COMET: A Quality Metric for Simultaneous Interpretation in Distant Language Pair Considering Word Order Difference

In fine-grained sparse Mixture-of-Experts (MoE) models, a large pool of specialized experts replaces a small homogeneous set, shifting performance and throughput to be governed by inference-time expert activation. Yet most existing optimization recipes implicitly assume a fixed activation budget (e.g., a constant Top-$k$ per layer), whose behavior in fine-grained MoEs is poorly understood. We first characterize runtime skipping strategies, quantifying the accuracy--efficiency trade-off of (i) uniform fixed activation and (ii) static layer-wise Top-$k$ allocation found by search. Our analysis reveals that static skipping can already provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms. We therefore introduce Adaptive Skipping with Entropy-Penalized Thresholding (ASET), a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model's original budget. Across the fine-grained MoEs we study, static skipping policies yield 10--78\% throughput gains with minimal performance degradation, including $\ge$10\% improvement on DeepSeek-V3 without measurable loss. On the OLMoE testbed, ASET yields a Pareto frontier between average activation and task quality. Overall, these results identify expert skipping as a practical lever for faster fine-grained MoE inference, with adaptive activation helping when fixed budgets are too rigid.

Faster MoE LLM Inference for Extremely Large Models

Large language models (LLMs), like human language learners, show patterns influenced by their dominant training language. Just as humans display language patterns influenced by their native tongue (semantic accents) when learning new languages, LLMs often default to English-centric responses even when generating in other languages. However, we observe that explicitly providing cultural context in prompts significantly improves the models' ability to generate culturally localized responses. We term this phenomenon the \emph{explicit-implicit localization gap}, indicating that while cultural knowledge exists within LLMs, it may not naturally surface in multilingual interaction without explicitly including cultural context. In this paper, we (1) quantify this gap in multiple LLMs using a new cultural localization benchmark and find large (>10%) gaps in the majority of investigated models. (2) Demonstrate a fundamental trade-off between localization accuracy and output diversity. (3) Through mechanistic interpretability, we identify the underlying localization mechanisms within LLMs and show that these mechanisms are both language and task agnostic, with individual steering vectors effectively generalizing across different languages and culturally-relevant tasks.

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models

Large Language Models (LLMs) have demonstrated strong cross-domain capabilities, yet their competence in specialized professional tasks remains underexamined. Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice. To bridge this gap, we construct JurisBench, a vertical, depth-oriented, domain-specific benchmark designed to evaluate LLMs across key stages of Chinese civil litigation. JurisBench introduces a Linear Depth Simulation track that mirrors the cognitive workflow of professional judges through four sequential, dependency-aware phases: Cause of Action prediction, Focus of Disputes identification, Rationale of the Judgment generation, and Result of the Judgment determination. Results reveal an “illusion of competence”: state-of-the-art models exhibit marked performance degradation in end-to-end pipelines due to cascading error propagation. We identify precise statutory grounding as a persistent bottleneck, highlighting a critical gap between fluent linguistic output and judicial reliability. JurisBench shifts evaluation from isolated legal knowledge to workflow-level task execution, providing a diagnostic framework for legal AI and a template for benchmark design in specialized domains.

JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice

Transformer language model (LM) contextualization mechanisms remain poorly understood. Here, we leverage lexical ambiguity, where a single word has multiple meanings, to isolate attention mechanisms that contribute to word sense disambiguation. We take a ``'developmental' approach: first, using publicly available checkpoints for the Pythia models, we identify inflection points in disambiguation performance across each model in the suite; in 14M and 410M specifically, we identify attention heads whose attention to disambiguating words covaries with overall disambiguation performance across development. We then stress-test the robustness of these heads to stimulus perturbations: in 14M, we find limited robustness, but in 410M, we identify multiple heads with surprisingly generalizable behavior, including the part-of-speech of the disambiguating cue. Then, in a causal analysis, we find that ablating the target heads demonstrably impairs disambiguation performance, particularly in 14M. The developmental analyses of 14M are also reproduced across all random seeds of the model. Together, these results suggest: first, that disambiguation benefits from a constellation of mechanisms, some of which (especially in 14M) are highly sensitive to the position and part-of-speech of the disambiguating cue; and second, that larger models (410M) may contain heads with more robust disambiguation behavior. They also join a growing body of work that highlights the value of adopting a developmental perspective when probing LM mechanisms.

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Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

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