United States

The quality of pre-training data critically impacts the capabilities of large language models. Existing pipelines rely on expert-crafted heuristic rules, which primarily operate at the sample level and are based on coarse statistical indicators, thus lacking content-aware, fine-grained noise detection. While recent generative approaches, e.g., ProX-C, enable token-level refinement, their reliance on synthesizing Python code incurs prohibitive computational cost at scale and can introduce hallucinations into the refined data. To overcome these limitations, we propose Selecting over Tokens (SelecT), a novel framework that reframes data refinement as a highly efficient token classification task. SelecT classifies each token as either informative or noisy and subsequently removes the latter. This design achieves fine-grained data optimization while avoiding the inefficiency of generation, ensuring scalability. When evaluated on diverse downstream benchmarks, the model trained on SelecT-refined corpora, on average, outperforms the one trained on raw data by over 2\% and exceeds the best heuristic baselines by more than 1\% while preserving 17\% more tokens than the latter. Furthermore, SelecT achieves higher average performance than the generative ProX-C across all experimental settings, and is 2.5x faster at inference, even with twice the parameters. Our results establish SelecT as an effective, efficient, and scalable solution for pre-training data optimization.

ACL 2026

SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

technical paper

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

Address intelligence in e-commerce demands accurate geocoding and proactive defect detection under strict sub-$50$~ms latency constraints. These tasks are inherently coupled: precise spatial grounding provides a strong prior for defect propensity, yet prior approaches optimize them independently. While generative LLMs offer rich semantic representations, they lack spatial inductive bias and fail to meet real-time serving requirements. We introduce GeoGround, a multi-task learning framework that jointly models coordinate grounding and address defect detection. The model combines a hierarchical spatial grounding objective with Focal Loss for defect classification, using uncertainty-based task weighting to balance optimization under severe class imbalance. To strengthen supervision, we curate a large-scale noisy address dataset using LLM-assisted data construction, augmenting the training corpus with signals that are costly to obtain manually. GeoGround achieves 5.86$\times$ gains in address defect detection precision and up to 4.86$\times$ improvements in location prediction accuracy over strong encoder baselines, while remaining 75$\times$ more efficient than decoder LLMs such as Qwen2-1.5B. A two-week online A/B test in a large-scale delivery pipeline confirms real-world impact, yielding a 50 bps uplift in defect detection, a 40 bps gain in location prediction, and an estimated operational savings of \$3.09M annually.

GeoGround: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection

Short-video platforms now present tappable search entries beneath the video player, making it effortless for users to shift from passively watching to actively searching for information. Prior work on bottom-bar query generation conditions on titles and OCR to generate a single query per forward pass, constrains decoding with a trie, and evaluates against a single reference using edit-distance–style supervision—making it difficult to cover the diverse intents a video can trigger and to credit semantically equivalent query variants. Motivated by these limitations, we propose four complementary improvements. First, we reformulate the task as one-shot list generation, producing multiple distinct queries per video, and build multi-query ground truth from exposure and CTR logs. Second, we redesign offline evaluation with $\operatorname{CTR\text{-}HungF1}$, a CTR-weighted set-matching metric via optimal assignment over token-level F1 score. Third, we enrich context with a video-to-video-to-query (V2V2Q) RAG pipeline to provide behavior-grounded background knowledge. Finally, we apply thinking-free RLVR with deterministic format checks and $\operatorname{CTR\text{-}HungF1}$ rewards to train a compact LLM without reward models or CoT distillation. The resulting system yields strong offline and online improvements, and has been deployed on Kuaishou to serve hundreds of millions of users daily.

From Short Video to Clickable Search: RLVR-Enabled Listwise Query Suggestion with Retrieval-Augmented Context

With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution

As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through rigorous comparison across weight-activation and KV-cache tasks, we provide three key insights: (1) INT8 suits narrow-range data, while floating-point formats excel with high-variance data; (2) in 4-bit regimes, HiF4’s hierarchical scaling prevents the accuracy collapse seen in integer formats; and (3) HiFloat is fully compatible with state-of-the-art post-training quantization frameworks. Overall, HiFloat provides a solution for high-efficiency LLM inference on NPUs.

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats

Credit risk models suffer from rapid performance decay due to distribution shifts, requiring frequent updates to meet strict operational guardrails. However, manual refreshing takes weeks of trial-and-error across upstream data engineering and downstream training. We present ACRM, a deployed multi-agent framework that automates the end-to-end credit modeling workflow by treating it as a learnable trajectory of agent interactions. Unlike AutoML, which optimizes hyperparameters on fixed datasets, ACRM's action space extends to upstream data semantics---cohort selection, observation windowing, feature screening---where the majority of performance recovery occurs. A central Orchestrator coordinates specialist agents through a three-stream decision stack: rule-based safety guardrails, retrieval-augmented grounding from historical workflows, and preference alignment via DPO on expert-labeled trajectories. Deployed at a major fintech institution for three months across six business scenarios, ACRM reduced the average model refresh cycle from weeks to 1.1 days and iteration rounds by 65%, while maintaining superior stability metrics.

ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production

The whole-page reranking integrates retrieval results from multiple modalities and is critical for user experience of search engines, yet it requires costly large-scale expert annotations due to the complexity of assessing cross-modal relevances. In this paper, we propose SMAR, a novel whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross modal annotations and distilling intra-modality preferences to align relevance scales across modalities. Specifically, we use pre-trained single-modal rankers to construct candidate pages for limited cross-modal annotation at the page level. The whole-page reranker is then trained on these samples, enforcing consistency with single-modal preferences to preserve intra-modal ranking quality. Experiments on the Qilin and CrossRank datasets demonstrate that SMAR reduces annotation costs by 70-90\% while outperforming the fully-annotated reranking baselines. Further offline and online A/B tests confirm significant gains in both ranking metrics and user experience, validating the effectiveness and practical value of our approach in real-world search scenarios.

An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision

In subscription-based businesses, understanding why a customer intends to churn is as vital as the classification itself. We present a case study at a major Dutch telecommunications provider, where we implement Text Bottleneck Models (TBMs) for post-call churn classification. The TBM distills dialogues into a sparse set of human-interpretable concepts and provides faithful, snippet-based evidence for every decision. We show that the TBM performs competitively with black-box baselines and demonstrate potential business impact via automated call profiling and an interactive stakeholder dashboard. Our work demonstrates that the perceived trade-off between interpretability and predictive performance can be bridged, providing the high-accuracy evidence needed for industrial retention strategies.

Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider

As a primary medium for human interaction and information exchange, social networking services (SNS) present distinct challenges for large language models (LLMs): rapidly evolving norms and slang, and culturally diverse content that causes knowledge distribution shift. While supervised fine-tuning (SFT) can improve in-domain performance, it often induces a "seesaw" trade-off with out-of-domain robustness, especially for smaller models. To address these challenges, we present RedOne 2.0, an SNS-oriented LLM developed with a progressive, RL-prioritized post-training paradigm for fast and stable adaptation. Our pipeline has three stages: (1) Exploratory Learning on curated SNS corpora to establish initial alignment and surface systematic weaknesses; (2) Targeted Fine-Tuning that applies SFT only to diagnosed gaps while mixing a small amount of general data to reduce forgetting; and (3) Refinement Learning that re-applies RL with SNS-centric signals to consolidate gains and balance trade-offs across tasks. Across various tasks in three categories, our 4B model improves by 2.41 on average over a sub-optimal 7B baseline. It also yields an 8.74 average gain over the base model while using less than half the data required by the SFT-centric method, demonstrating superior data efficiency and stability at compact scales. Overall, RedOne 2.0 provides a competitive, cost-effective baseline for SNS-specific LLMs, improving capability without sacrificing robustness.

RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services

Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a user’s behalf, also creates new risks for sensitive information leakage. 
We introduce **CI-Work**, a Contextual Integrity (CI)-grounded benchmark that simulates enterprise workflows across five information-flow directions and evaluates whether agents can convey *essential* content while withholding *sensitive* context in dense retrieval settings.
Our evaluation of frontier models reveals that privacy failures are prevalent (violation rates range from 15.8%-50.9%, with leakage reaching up to 26.7%) and uncovers a counterintuitive trade-off critical for industrial deployment: higher task utility often correlates with increased privacy violations.
Moreover, the massive scale of enterprise data and potential user behavior further amplify this vulnerability. Simply increasing model size or reasoning depth fails to address the problem. We conclude that safeguarding enterprise workflows requires a paradigm shift, moving beyond model-centric scaling toward context-centric architectures.

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents

Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. 
A widely adopted remedy is the hybrid system paradigm, which balances cost and quality by serving most requests with a small model and selectively routing a fraction to a large model. 
However, existing routing strategies often rely on heuristics, external predictors, or absolute quality estimation, which fail to capture whether the large model actually provides a worthwhile improvement over the small one. 
In this paper, we formulate routing as a budget allocation problem and identify marginal gain, i.e., the large model’s improvement over the small model, as the optimal signal for budgeted decisions. 
Building on this, we propose \textbf{RouteLMT} (routing for LLM-based MT), an efficient in-model router that predicts this expected gain by probing the small translator’s prompt-token representation, without requiring external models or hypothesis decoding. 
Extensive experiments demonstrate that our RouteLMT outperforms heuristics, quality/difficulty estimation baselines, achieving a superior quality–budget Pareto frontier. 
Furthermore, we analyze regression risks and show that a simple guarded variant can mitigate severe quality losses.

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