Computation and Language · Cross-listed from cs.AI
Budget-aware Test-time Scaling via Discriminative Verification
Kyle Montgomery, Sijun Tan, Yuqi Chen et al. (+4 more)
Test-time scaling is a powerful strategy for boosting the performance of large
language models on complex
reasoning tasks. While state-of-the-art approaches often employ generative verifiers to select the best solution from a pool of candidates, this method incurs prohibitive computational costs, limiting its practicality. In this work, we shift the focus to a more budget-aware paradigm: discriminative verification. We conduct a thorough empirical analysis and demonstrate that while discriminative verifiers may underperform in isolation, combining them with self-consistency in a hybrid approach creates a powerful and efficient test-time scaling mechanism. Notably, under a fixed compute budget, this hybrid approach surpasses state-of-the-art generative verification by a significant margin: achieving up to 15.3% higher accuracy on AIME2025. Our findings establish that for practical, real-world applications, budget-aware scaling with discriminative verifiers is not only a "free" upgrade over self-consistency, but also a more effective and efficient alternative to costly generative techniques. Code is available at https://github.com/wang-research-lab/verification.
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All authors Kyle Montgomery, Sijun Tan, Yuqi Chen, Siyuan Zhuang, Tianjun Zhang, Raluca Ada Popa, Chenguang Wang
Computation and Language · Cross-listed from cs.LG
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Aayush Karan and Yilun Du
Frontier
reasoning models have exhibited incredible capabilities across a wide array of disciplines, driven by posttraining large
language models (LLMs) with reinforcement learning (RL). However, despite the widespread success of this paradigm, much of the literature has been devoted to disentangling truly novel behaviors that emerge during RL but are not present in the base models. In our work, we approach this question from a different angle, instead asking whether comparable
reasoning capabilites can be elicited from base models at inference time by pure sampling, without any additional training. Inspired by Markov chain Monte Carlo (MCMC) techniques for sampling from sharpened distributions, we propose a simple iterative sampling
algorithm leveraging the base models' own likelihoods. Over different base models, we show that our
algorithm offers substantial boosts in
reasoning that nearly match and even outperform those from RL on a wide variety of single-shot tasks, including MATH500, HumanEval, and GPQA. Moreover, our sampler avoids the collapse in diversity over multiple samples that is characteristic of RL-posttraining. Crucially, our method does not require training, curated
datasets, or a verifier, suggesting broad applicability beyond easily verifiable domains.
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Computation and Language · Cross-listed from cs.SI
Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
Soorya Ram Shimgekar, Ruining Zhao, Agam Goyal et al. (+5 more)
On social media, several individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly. Instead, signs may surface indirectly through everyday posts or peer interactions. Detecting such implicit signals early is critical but remains challenging. We frame early and implicit SI as a forward-looking prediction task and develop a computational framework that models a user's information environment, consisting of both their longitudinal posting histories as well as the discourse of their socially proximal peers. We adopted a composite network centrality measure to identify top neighbors of a user, and temporally aligned the user's and neighbors' interactions – integrating the multi-layered signals in a fine-tuned DeBERTa-v3 model. In a Reddit study of 1,000 (500 Case and 500 Control) users, our approach improves early and implicit SI detection by an average of 10% over all other baselines. These findings highlight that peer interactions offer valuable predictive signals and carry broader implications for designing early detection systems that capture indirect as well as masked expressions of risk in online environments.
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All authors Soorya Ram Shimgekar, Ruining Zhao, Agam Goyal, Violeta J. Rodriguez, Paul A. Bloom, Navin Kumar, Hari Sundaram, Koustuv Saha
Computation and Language · Cross-listed from cs.CV
You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction
Logan Lawrence, Oindrila Saha, Megan Wei et al. (+3 more)
Despite the renewed interest in zero-shot visual classification due to the rise of
Multimodal Large
Language Models (MLLMs), the problem of evaluating free-form responses of auto-regressive models remains a persistent challenge. Most existing works focus on
language-only tasks or don't consider Multiple Choice Questions (MCQs) beyond 5-way options, both of which are critical capabilities to solve tasks in Fine-Grained Visual Classification (FGVC) where choice counts are in the hundreds to thousands and the choices are highly related. Furthermore, in this highly multi-way MCQ setting it is not clear how to extend LLM choice extraction to
retrieval-based problems, where computing probabilities over the choice set is computationally costly. In this work we investigate nlg2choice, a simple two-stage method which first asks the MLLM an open-ended question for the task with minimal constraints, then uses text-only constrained decoding to predict the most likely choice. In
retrieval settings, we compute the probability of the constrained response taking that choice with an early stopping method to significantly improve throughput. Our results show improvement over a suite of seven fine-grained visual
datasets when evaluating in terms of classification and
retrieval, and show that this performance holds over the various ways that users of LLMs can implement tasks in natural
language.
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All authors Logan Lawrence, Oindrila Saha, Megan Wei, Chen Sun, Subhransu Maji, Grant Van Horn
Computation and Language
From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR
Erwei Wang, Samuel Bayliss, Andra Bisca et al. (+19 more)
General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures increasingly rely on fine-grained control over data movement, execution order, and compute placement for performance, compiler infrastructure must provide explicit mechanisms for orchestrating compute and data to fully exploit such architectures. We introduce MLIR-AIR, a novel, open-source compiler stack built on MLIR that bridges the semantic gap between high-level workloads and fine-grained spatial architectures such as AMD's NPUs. MLIR-AIR defines the AIR dialect, which provides structured representations for asynchronous and hierarchical operations across compute and
memory resources. AIR primitives allow the compiler to orchestrate spatial scheduling, distribute computation across hardware regions, and overlap communication with computation without relying on ad hoc runtime coordination or manual scheduling. We demonstrate MLIR-AIR's capabilities through two case studies:
matrix multiplication and the multi-head
attention block from the LLaMA 2 model. For
matrix multiplication, MLIR-AIR achieves up to 78.7% compute efficiency and generates implementations with performance almost identical to state-of-the-art, hand-optimized
matrix multiplication written using the lower-level, close-to-metal MLIR-AIE framework. For multi-head
attention, we demonstrate that the AIR interface supports fused implementations using approximately 150 lines of code, enabling tractable expression of complex workloads with efficient mapping to spatial hardware. MLIR-AIR transforms high-level structured control flow into spatial programs that efficiently utilize the compute fabric and
memory hierarchy of an NPU, leveraging asynchronous execution, tiling, and communication overlap through compiler-managed scheduling.
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All authors Erwei Wang, Samuel Bayliss, Andra Bisca, Zachary Blair, Sangeeta Chowdhary, Kristof Denolf, Jeff Fifield, Brandon Freiberger, Erika Hunhoff, Phil James-Roxby, Jack Lo, Joseph Melber, Stephen Neuendorffer, Eddie Richter, Andre Rosti, Javier Setoain, Gagandeep Singh, Endri Taka, Pranathi Vasireddy, Zhewen Yu, Niansong Zhang, Jinming Zhuang
Computation and Language · Cross-listed from cs.CV
Benchmarking Multimodal Large Language Models for Face Recognition
Hatef Otroshi Shahreza and Sébastien Marcel
Multimodal large
language models (MLLMs) have achieved remarkable performance across diverse
vision-and-
language tasks. However, their potential in face recognition remains underexplored. In particular, the performance of open-source MLLMs needs to be evaluated and compared with existing face recognition models on standard
benchmarks with similar protocol. In this work, we present a systematic
benchmark of state-of-the-art MLLMs for face recognition on several face recognition
datasets, including LFW, CALFW, CPLFW, CFP, AgeDB and RFW. Experimental results reveal that while MLLMs capture rich semantic cues useful for face-related tasks, they lag behind specialized models in high-precision recognition scenarios in zero-shot applications. This
benchmark provides a foundation for advancing MLLM-based face recognition, offering insights for the design of next-generation models with higher accuracy and generalization. The source code of our
benchmark is publicly available in the project page.
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Computation and Language
Midtraining Bridges Pretraining and Posttraining Distributions
Emmy Liu, Graham Neubig and Chenyan Xiong
Midtraining, the practice of mixing specialized data with more general pretraining data in an intermediate training phase, has become widespread in
language model development, yet there is little understanding of what makes it effective. We propose that midtraining functions as distributional bridging by providing better initialization for posttraining. We conduct controlled pretraining experiments, and find that midtraining benefits are largest for domains distant from general pretraining data, such as code and math, and scale with the proximity advantage the midtraining data provides toward the target distribution. In these domains, midtraining consistently outperforms continued pretraining on specialized data alone both in-domain and in terms of mitigating forgetting. We further conduct an investigation on the starting time and mixture weight of midtraining data, using code as a case study, and find that time of introduction and mixture weight interact strongly such that early introduction of specialized data is amenable to high mixture weights, while late introduction requires lower ones. This suggests that late introduction of specialized data outside a plasticity window cannot be compensated for by increasing data mixtures later in training. Beyond midtraining itself, this suggests that distributional transitions between any training phases may benefit from similar bridging strategies.
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Computation and Language
Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models
Guinan Su, Yanwu Yang, Li Shen et al. (+3 more)
Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deployment. While existing test-time adaptation methods could potentially address these issues, they primarily focus on dense models and require access to external data, limiting their practical applicability to MoE architectures. However, we find that, instead of relying on reference data, we can optimize MoE expert selection on-the-fly based only on input context. As such, we propose a data-free, online test-time framework that continuously adapts MoE routing decisions during text generation without external supervision or data. Our method cycles between two phases: During the prefill stage, and later in regular intervals, we optimize the routing decisions of the model using self-supervision based on the already generated sequence. Then, we generate text as normal, maintaining the modified router until the next adaption. We implement this through lightweight additive vectors that only update router logits in selected layers, maintaining computational efficiency while preventing over-adaptation. The experimental results show consistent performance gains on challenging
reasoning tasks while maintaining robustness to context shifts. For example, our method achieves a 5.5% improvement on HumanEval with OLMoE. Furthermore, owing to its plug-and-play property, our method naturally complements existing test-time scaling techniques, e.g., achieving 6% average gains when incorporated with self-consistency on DeepSeek-V2-Lite.
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All authors Guinan Su, Yanwu Yang, Li Shen, Lu Yin, Shiwei Liu, Jonas Geiping
Computation and Language · Cross-listed from cs.AI
Where to Search: Measure the Prior-Structured Search Space of LLM Agents
Zhuo-Yang Song
The generate-filter-refine (iterative paradigm) based on large
language models (LLMs) has achieved progress in
reasoning, programming, and program discovery in AI+Science. However, the effectiveness of search depends on where to search, namely, how to encode the domain prior into an operationally structured hypothesis space. To this end, this paper proposes a compact formal theory that describes and measures LLM-assisted iterative search guided by domain priors. We represent an
agent as a fuzzy relation operator on inputs and outputs to capture feasible transitions; the
agent is thereby constrained by a fixed safety envelope. To describe multi-step
reasoning/search, we weight all reachable paths by a single continuation parameter and sum them to obtain a coverage generating function; this induces a measure of reachability difficulty; and it provides a
geometric interpretation of search on the graph induced by the safety envelope. We further provide the simplest testable inferences and validate them via two instantiation. This theory offers a workable
language and operational tools to measure
agents and their search spaces, proposing a systematic formal description of iterative search constructed by LLMs.
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Computation and Language
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li et al. (+6 more)
In information
retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant query-document pairs) and classification (binary label prediction of relevance vs. irrelevance). For BERT-style encoders, various studies have shown that contrastive learning (CL) can be more effective than discriminative (classification) learning. However, for large
language models (LLMs), classification via supervised fine-tuning (SFT), which predicts ”yes” (resp. ”no”)
token for relevant (resp. irrelevant) pairs, appears more promising as it aligns well with the generative nature of LLMs. This divergence raises a central question: which objective is intrinsically better suited to LLM-based reranking, and what mechanism underlies the difference? In this work, we conduct a comprehensive comparison and analysis between CL and SFT for reranking, taking the universal
multimodal retrieval (UMR) as the experimental playground. We first decompose the objectives into two components: weight, which controls the magnitude of those updates, and direction, which guides the model updates, then present a unified framework for understanding their interactions. Through probing experiments, we find that SFT provides a substantially stronger weighting scheme than CL, whereas the preferred scoring direction shows no clear winner. Taken together, these results point to a consistent advantage of SFT over CL for LLM reranking. To further validate our findings, we conduct large-scale training with SFT and present new state-of-the-art rerankers on the MRB
benchmark. We also provide ablations on SFT settings and expect our findings to benefit future research and applications in this area.
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All authors Ziqi Dai, Xin Zhang, Mingxin Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, Min Zhang
Computation and Language
Finding Answers in Thought Matters: Revisiting Evaluation on Large Language Models with Reasoning
Hwiyeol Jo, Joosung Lee, Jaehone Lee et al. (+3 more)
Evaluating generative models, such as large
language models (LLMs), commonly involves question-answering tasks where the final answer is selected based on probability of answer choices. On the other hand, for models requiring
reasoning, the method of answer extraction plays a critical role. Our research reveals that the performance of
reasoning models and their final answer distributions are highly sensitive to the answer extraction
algorithm employed. In order to mitigate this, we propose a basic framework: Answer Regeneration. The method uses an additional model inference, providing the prior input and output prefaced by the
prompt "Answer:". The final answer is then selected or extracted from the regenerated output. We show that this extraction-rule-agnostic approach exhibits improved performance and enhanced robustness. Furthermore, we have applied this framework to general math problems and open-ended question answering tasks. Our analysis and this framework could offer a more reliable results for model evaluation.
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All authors Hwiyeol Jo, Joosung Lee, Jaehone Lee, Sang-Woo Lee, Joonsuk Park, Kang Min Yoo
Computation and Language
COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes
Yunwen Li, Shuangshuang Ying, Xingwei Qu et al. (+16 more)
Large
language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. We present COIG-Writer, a novel Chinese creative writing
dataset that captures both diverse outputs and their underlying thought processes through systematic reverse-engineering of high-quality texts. Unlike existing
datasets that provide only input-output pairs, COIG-Writer comprises 1,665 meticulously curated triplets spanning 51 genres, each containing: (1) a reverse-engineered
prompt, (2) detailed creative
reasoning documenting decision-making processes, and (3) the final text. Through comprehensive experiments, we identify a two-component model of creative writing: narrative logic (provided by process supervision) and linguistic expression (maintained by general-purpose data). Our findings reveal three critical insights: (1) Process supervision is highly effective but requires stabilization with general data. A ratio of at least one creative sample to twelve general samples is needed to achieve optimal performance; below this threshold, the win rate progressively degrades (from 62.75% down to 35.78%)., (2) creative capabilities are culturally-bound with no cross-lingual transfer (89.26pp gap between Chinese and English performance), and (3) lexical diversity inversely correlates with creative quality (TTR paradox), suggesting high diversity signals compensatory behavior for logical deficiencies. These findings establish that creative excellence emerges from the interaction between logical scaffolding and linguistic grounding, analogous to how mathematical
reasoning enhances but cannot replace linguistic competence in foundation models.
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All authors Yunwen Li, Shuangshuang Ying, Xingwei Qu, Xin Li, Sheng Jin, Minghao Liu, Zhoufutu Wen, Tianyu Zheng, Xeron Du, Qiguang Chen, Jiajun Shi, Wangchunshu Zhou, Jiazhan Feng, Wanjun Zhong, Libo Qin, Stephen Huang, Wanxiang Che, Chenghua Lin, Eli Zhang
Computation and Language
Pluto: A Benchmark for Evaluating Efficiency of LLM-generated Hardware Code
Manar Abdelatty, Maryam Nouh, Jacob K. Rosenstein et al. (+1 more)
Large
Language Models (LLMs) are increasingly used to automate hardware design tasks, including the generation of Verilog code. While early
benchmarks focus primarily on functional correctness, efficient hardware design demands additional optimization for synthesis metrics such as area, delay, and power. Existing
benchmarks fall short in evaluating these aspects comprehensively: they often lack optimized baselines or testbenches for verification. To address these gaps, we present Pluto, a
benchmark and evaluation framework designed to assess the efficiency of LLM-generated Verilog designs. Pluto presents a comprehensive evaluation set of 114 problems with self-checking testbenches and multiple Pareto-optimal reference implementations. Experimental results show that state-of-the-art LLMs can achieve high functional correctness, reaching 78.3% at pass@1, but their synthesis efficiency still lags behind expert-crafted implementations, with area efficiency of 63.8%, delay efficiency of 65.9%, and power efficiency of 64.0% at eff@1. This highlights the need for efficiency-aware evaluation frameworks such as Pluto to drive progress in hardware-focused LLM research.
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All authors Manar Abdelatty, Maryam Nouh, Jacob K. Rosenstein, Sherief Reda
Computation and Language
AutoRubric: Rubric-Based Generative Rewards for Faithful Multimodal Reasoning
Mengzhao Jia, Zhihan Zhang, Ignacio Cases et al. (+3 more)
Multimodal large
language models (MLLMs) have rapidly advanced from perception tasks to complex multi-step
reasoning, yet reinforcement learning with verifiable rewards (RLVR) often leads to spurious
reasoning since only the final-answer correctness is rewarded. To address this limitation, we propose AutoRubric, a framework that integrates RLVR with process-level supervision through automatically collected rubric-based generative rewards. Our key innovation lies in a scalable self-aggregation method that distills consistent
reasoning checkpoints from successful trajectories, enabling problem-specific rubric construction without human annotation or stronger teacher models. By jointly leveraging rubric-based and outcome rewards, AutoRubric achieves state-of-the-art performance on six
multimodal reasoning benchmarks and substantially improves
reasoning faithfulness in dedicated evaluations.
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All authors Mengzhao Jia, Zhihan Zhang, Ignacio Cases, Zheyuan Liu, Meng Jiang, Peng Qi
Computation and Language
Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI
Xingmeng Zhao, Tongnian Wang, Dan Schumacher et al. (+2 more)
Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, rapid and low-barrier development can introduce risks of bias,
privacy violations, and unequal access, especially when systems ignore real-world contexts and diverse user needs. Many recent methods use AI to detect risks automatically, but this can reduce human engagement in understanding how harms arise and who they affect. We present a human-centered framework that generates user stories and supports multi-
agent discussions to help people think creatively about potential benefits and harms before deployment. In a user study, participants who read stories recognized a broader range of harms, distributing their responses more evenly across all 17 harm types. In contrast, those who did not read stories focused primarily on
privacy and well-being (79.1%). Our findings show that storytelling helped participants speculate about a broader range of harms and benefits and think more creatively about AI's impact on users.
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All authors Xingmeng Zhao, Tongnian Wang, Dan Schumacher, Veronica Rammouz, Anthony Rios
Computation and Language · Cross-listed from cs.AI
TITAN: Graph-Executable Reasoning for Cyber Threat Intelligence
Marco Simoni, Aleksandar Fontana, Andrea Saracino et al. (+1 more)
TITAN (Threat Intelligence Through Automated
Navigation) is a framework that connects natural-
language cyber threat queries with executable
reasoning over a structured knowledge graph. It integrates a path planner model, which predicts logical relation chains from text, and a graph executor that traverses the TITAN Ontology to retrieve factual answers and supporting evidence. Unlike traditional
retrieval systems, TITAN operates on a typed, bidirectional graph derived from MITRE, allowing
reasoning to move clearly and reversibly between threats, behaviors, and defenses. To support training and evaluation, we introduce the TITAN
Dataset, a corpus of 88209 examples (Train: 74258; Test: 13951) pairing natural
language questions with executable
reasoning paths and step by step Chain of Thought explanations. Empirical evaluations show that TITAN enables models to generate syntactically valid and semantically coherent
reasoning paths that can be deterministically executed on the underlying graph.
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All authors Marco Simoni, Aleksandar Fontana, Andrea Saracino, Paolo Mori
Computation and Language
Semantic Prosody in Machine Translation: the English-Chinese Case of Passive Structures
Xinyue Ma, Pol Pastells, Mireia Farrús et al. (+1 more)
Semantic prosody is a collocational meaning formed through the co-occurrence of a linguistic unit and a consistent series of collocates, which should be treated separately from semantic meaning. Since words that are literal translations of each other may have different semantic prosody, more
attention should be paid to this linguistic property to generate accurate translations. However, current machine translation models cannot handle this problem. To bridge the gap, we propose an approach to teach machine translation models about semantic prosody of a specific structure. We focus on Chinese BEI passives and create a
dataset of English-Chinese sentence pairs with the purpose of demonstrating the negative semantic prosody of BEI passives. Then we fine-tune OPUS-MT, NLLB-600M and mBART50 models with our
dataset for the English-Chinese translation task. Our results show that fine-tuned MT models perform better on using BEI passives for translating unfavourable content and avoid using it for neutral and favourable content. Also, in NLLB-600M, which is a
multilingual model, this knowledge of semantic prosody can be transferred from English-Chinese translation to other
language pairs, such as Spanish-Chinese.
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All authors Xinyue Ma, Pol Pastells, Mireia Farrús, Mariona Taulé
Computation and Language
An Efficient Rubric-based Generative Verifier for Search-Augmented LLMs
Linyue Ma, Yilong Xu, Xiang Long et al. (+1 more)
Search augmentation empowers Large
Language Models with
retrieval capabilities to overcome the limitations imposed by static parameters. Recently, Reinforcement Learning leverages tailored reward signals as a viable technique to enhance LLMs performing tasks involving search. However, existing reward modeling for search-augmented LLMs faces several limitations. Rule-based rewards, such as Exact Match, are verifiable but fragile to variations in expression and cannot be applied to long-form workloads. In contrast, generative rewards improve robustness, but designing verifiable and stable rewards for long-form workloads in dynamic corpora remains challenging and also incurs high computational costs. In this paper, we propose a unified and verifiable paradigm, "nugget-as-rubric", which treats atomic information points as structured evaluation criteria for different search-augmentation workloads. Short-form tasks correspond to a single rubric, whereas long-form tasks expand to multiple rubrics aligned with the question's information needs. To support long-form settings, we design an automatic rubric construction pipeline based on query rewriting, which can automatically retrieve passages relevant to each question and extract rubrics from them, both from static corpora and from dynamic online web content. Furthermore, we introduce Search-Gen-V, a 4B-parameter efficient generative verifier under our proposed verifiable paradigm, which is trained via the idea of
distillation and a two-stage strategy. Experimental results show that Search-Gen-V achieves strong verification accuracy across different workloads, making it a scalable, robust, and efficient verifiable reward constructor for search-augmented LLMs.
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All authors Linyue Ma, Yilong Xu, Xiang Long, Zhi Zheng
Computation and Language
LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
I-Fan Lin, Faegheh Hasibi and Suzan Verberne
In this paper, we propose an intuitive, training-free and label-free method for intent clustering in conversational search. Current approaches to short text clustering use LLM-generated pseudo-labels to enrich text representations or to identify similar text pairs for pooling. The limitations are: (1) each text is assigned only a single label, and refining representations toward a single label can be unstable; (2) text-level similarity is treated as a binary selection, which fails to account for continuous degrees of similarity. Our method LUMI is designed to amplify similarities between texts by using shared pseudo-labels. We first generate pseudo-labels for each text and collect them into a pseudo-label set. Next, we compute the mean of the pseudo-label
embeddings and pool it with the text
embedding. Finally, we perform text-level pooling: Each text representation is pooled with its similar pairs, where similarity is determined by the degree of shared labels. Our evaluation on four
benchmark sets shows that our approach achieves competitive results, better than recent state-of-the-art baselines, while avoiding the need to estimate the number of clusters during
embedding refinement, as is required by most methods. Our findings indicate that LUMI can effectively be applied in unsupervised short-text clustering scenarios.
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Computation and Language
RLAIF-SPA: Structured AI Feedback for Semantic-Prosodic Alignment in Speech Synthesis
Qing Yang, Zhenghao Liu, Yangfan Du et al. (+2 more)
Recent advances in Text-To-
Speech (TTS) synthesis have achieved near-human
speech quality in neutral speaking styles. However, most existing approaches either depend on costly
emotion annotations or optimize surrogate objectives that fail to adequately capture perceptual emotional quality. As a result, the generated
speech, while semantically accurate, often lacks expressive and emotionally rich characteristics. To address these limitations, we propose RLAIF-SPA, a novel framework that integrates Reinforcement Learning from AI Feedback (RLAIF) to directly optimize both emotional expressiveness and intelligibility without human supervision. Specifically, RLAIF-SPA incorporates Automatic
Speech Recognition (ASR) to provide semantic accuracy feedback, while leveraging structured reward modeling to evaluate prosodic-emotional consistency. RLAIF-SPA enables more precise and nuanced control over expressive
speech generation along four structured evaluation dimensions: Structure,
Emotion, Speed, and Tone. Extensive experiments on Libri-
Speech, MELD, and Mandarin ESD
datasets demonstrate consistent gains across clean read
speech, conversational dialogue, and emotional
speech. On Libri-
Speech, RLAIF-SPA consistently outperforms Chat-TTS, achieving a 26.1% reduction in word error rate, a 9.1% improvement in SIM-O, and over 10% gains in human subjective evaluations.
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All authors Qing Yang, Zhenghao Liu, Yangfan Du, Pengcheng Huang, Tong Xiao
Computation and Language · Cross-listed from cs.AI
ColorBench: Benchmarking Mobile Agents with Graph-Structured Framework for Complex Long-Horizon Tasks
Yuanyi Song, Heyuan Huang, Qiqiang Lin et al. (+10 more)
The rapid advancement of
multimodal large
language models has enabled
agents to operate mobile devices by directly interacting with graphical user interfaces, opening new possibilities for mobile automation. However, real-world mobile tasks are often complex and allow for multiple valid solutions. This contradicts current mobile
agent evaluation standards: offline static
benchmarks can only validate a single predefined "golden path", while online dynamic testing is constrained by the complexity and non-reproducibility of real devices, making both approaches inadequate for comprehensively assessing
agent capabilities. To bridge the gap between offline and online evaluation and enhance testing stability, this paper introduces a novel graph-structured benchmarking framework. By modeling the finite states observed during real-device interactions, it achieves static simulation of dynamic behaviors. Building on this, we develop ColorBench, a
benchmark focused on complex long-horizon tasks. It supports evaluation of multiple valid solutions, subtask completion rate statistics, and atomic-level capability analysis. ColorBench contains 175 tasks (74 single-app, 101 cross-app) with an average length of over 13 steps. Each task includes at least two correct paths and several typical error paths, enabling quasi-dynamic interaction. By evaluating ColorBench across various baselines, we discover limitations of existing models and propose improvement directions and feasible technical pathways to enhance
agents' performance on complex, long-horizon problems based on experimental results. Code and data are available at: https://github.com/MadeAgents/ColorBench.
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All authors Yuanyi Song, Heyuan Huang, Qiqiang Lin, Yin Zhao, Xiangmou Qu, Jun Wang, Xingyu Lou, Weiwen Liu, Zhuosheng Zhang, Jun Wang, Yong Yu, Weinan Zhang, Zhaoxiang Wang
Computation and Language
Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Models
Kedi Chen, Zhikai Lei, Xu Guo et al. (+10 more)
Large
language models (LLMs) make remarkable progress in
reasoning tasks. Among different
reasoning modes, inductive
reasoning, due to its better
alignment with human learning, attracts increasing interest. However, research on inductive
reasoning faces certain challenges. First, existing inductive data mostly focuses on superficial regularities while lacking more complex internal patterns. Second, current works merely
prompt LLMs or finetune on simple
prompt-response pairs, but do not provide precise thinking processes nor implement difficulty control. Unlike previous work, we address these challenges by introducing CodeSeq, a synthetic post-training
dataset built from number sequences. We package number sequences into algorithmic problems to discover their general terms, defining a general term generation (GTG) task correspondingly. Our pipeline generates supervised finetuning data by reflecting on failed test cases and incorporating iterative corrections, thereby teaching LLMs to learn autonomous case generation and self-checking. Additionally, it leverages reinforcement learning with a novel Case-Synergy Solvability Scaling Reward based on both solvability, estimated from the problem pass rate, and the success rate of self-directed case generation, enabling models to learn more effectively from both successes and failures. Experimental results show that the models trained with CodeSeq improve on various
reasoning tasks and can preserve the models' OOD performance.
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All authors Kedi Chen, Zhikai Lei, Xu Guo, Xuecheng Wu, Siyuan Zeng, Jianghao Yin, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Qipeng Guo, Kai Chen, Wei Zhang
Computation and Language
Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures
Shuangshuang Ying, Yunwen Li, Xingwei Qu et al. (+21 more)
Current preference learning methods achieve high accuracy on standard
benchmarks but exhibit significant performance degradation when objective quality signals are removed. We introduce WritingPreferenceBench, a
dataset of 1,800 human-annotated preference pairs (1,200 English, 600 Chinese) across 8 creative writing genres, where responses are matched for objective correctness, factual accuracy, and length. On this
benchmark, sequence-based reward models–the standard architecture for RLHF–achieve only 52.7% mean accuracy, while zero-shot
language model judges perform at 53.9%. In contrast, generative reward models that produce explicit
reasoning chains achieve 81.8% accuracy. We observe high within-model variance across genres: individual models range from 18.2% to 81.8% accuracy across different writing categories, with standard deviations averaging 10.1%. This variance persists regardless of model scale, with 27B parameter models showing no consistent improvement over 8B variants. Our results suggest that current RLHF methods primarily learn to detect objective errors rather than capture subjective quality preferences (e.g., creativity, stylistic flair, and emotional resonance), and that successful preference modeling may require intermediate
reasoning representations rather than direct classification.
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All authors Shuangshuang Ying, Yunwen Li, Xingwei Qu, Xin Li, Sheng Jin, Minghao Liu, Zhoufutu Wen, Xeron Du, Tianyu Zheng, Yichi Zhang, Letian Ni, Yuyang Cheng, Zhenzhu Yang, Qiguang Chen, Jingzhe Ding, Shengda Long, Wangchunshu Zhou, Jiazhan Feng, Wanjun Zhong, Libo Qin, Ge Zhang, Wenhao Huang, Wanxiang Che, Chenghua Lin
Computation and Language · Cross-listed from cs.HC
Just-In-Time Objectives: A General Approach for Specialized AI Interactions
Michelle S. Lam, Omar Shaikh, Hallie Xu et al. (+5 more)
Large
language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user's in-the-moment objective, then rapidly optimizing for that singular objective, enables LLMs to produce specialized tools, interfaces, and responses. Our work introduces just-in-time objectives, which model a user's goals to specialize LLM systems on the fly. We contribute an architecture for automatically inducing such objectives by passively observing user behavior, then steering downstream AI systems through generation and evaluation against this objective. Inducing just-in-time objectives (e.g., "Clarify the abstract's research contribution") enables automatic generation of tools, e.g., those that critique a draft based on relevant HCI methodologies, anticipate related researchers' reactions, or surface ambiguous terminology. In a series of experiments on participants' own tasks, JIT objectives enable LLM outputs that achieve 66-86% win rates over typical LLMs. In-person use sessions confirm that JIT objectives produce specialized tools that are unique to each participant and are rated as significantly higher quality than a standard LLM chat tool.
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All authors Michelle S. Lam, Omar Shaikh, Hallie Xu, Alice Guo, Diyi Yang, Jeffrey Heer, James A. Landay, Michael S. Bernstein
Computation and Language · Cross-listed from cs.CV
Talking Points: Describing and Localizing Pixels
Matan Rusanovsky, Shimon Malnick and Shai Avidan
Vision-
language models have achieved remarkable success in cross-modal understanding. Yet, these models remain limited to object-level or region-level grounding, lacking the capability for pixel-precise keypoint comprehension through natural
language. We introduce a novel framework for pixel level grounding. The framework consists of two complementary components: a Point Descriptor that generates rich, contextual descriptions of individual keypoints, and a Point Localizer that regresses precise pixel coordinates from these descriptions. Unlike prior work that relies on templated
prompts or keypoint names, our approach produces free-form, coarse-to-fine descriptions that situate keypoints within their visual context. Since there is no available
dataset to train such a system, we introduce LlamaPointInPart, a carefully curated
dataset of 20K+ image-keypoint-description triplets synthesized from multiple
vision-
language models, capturing multi-scale information from scene-level context to visual features around the keypoint. For cross-category generalization, we optimize the Point Descriptor on AP-10K via GRPO, using the frozen Point Localizer as a reward model to produce descriptions that maximize localization accuracy. To evaluate our results we establish a new evaluation protocol. Instead of comparing the text description produced by our method to the ground truth, we use the localizer to determine how close is the predicted point generated to the ground truth point. Experiments demonstrate superior performance compared to baseline models on LlamaPointInPart.The bidirectional nature of our framework should enable future applications in both keypoint-guided image understanding and
language-guided precise localization. Our code and
dataset are publicly available at https://github.com/matanr/Talking_Points.
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Computation and Language
Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs
Kyubyung Chae, Gihoon Kim, Gyuseong Lee et al. (+3 more)
Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governments to develop their LLMs, tailored to their unique socio-cultural and historical contexts. However, there remains a shortage of frameworks and
datasets to verify two critical questions: (1) how well these models align with users' socio-cultural backgrounds, and (2) whether they maintain safety and technical robustness without exposing users to potential harms and risks. To address this gap, we construct a new
dataset and introduce an analytic framework for extracting and evaluating the socio-cultural elements of sovereign LLMs, alongside assessments of their technical robustness. Our experimental results demonstrate that while sovereign LLMs play a meaningful role in supporting low-resource
languages, they do not always meet the popular claim that these models serve their target users well. We also show that pursuing this untested claim may lead to underestimating critical quality attributes such as safety. Our study suggests that advancing sovereign LLMs requires a more extensive evaluation that incorporates a broader range of well-grounded and practical criteria.
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All authors Kyubyung Chae, Gihoon Kim, Gyuseong Lee, Taesup Kim, Jaejin Lee, Heejin Kim
Computation and Language · Cross-listed from cs.LG
Agentic Entropy-Balanced Policy Optimization
Guanting Dong, Licheng Bao, Zhongyuan Wang et al. (+11 more)
Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web
agents. While mainstream agentic RL
algorithms autonomously explore high-uncertainty tool-call steps under the guidance of
entropy, excessive reliance on
entropy signals can impose further constraints, leading to the training collapse. In this paper, we delve into the challenges caused by
entropy and propose the Agentic
Entropy-Balanced Policy Optimization (AEPO), an agentic RL
algorithm designed to balance
entropy in both the rollout and policy update phases. AEPO comprises two core components: (1) a dynamic
entropy-balanced rollout mechanism that adaptively allocate global and branch sampling budget through
entropy pre-monitoring, while imposing a branch penalty on consecutive high-
entropy tool-call steps to prevent over-branching issues; and (2)
Entropy-Balanced Policy Optimization that inserts a stop-gradient operation into the high-
entropy clipping term to preserve and properly rescale gradients on high-
entropy tokens, while incorporating
entropy-aware advantage estimation to prioritize learning on high-uncertainty
tokens. Results across 14 challenging
datasets show that AEPO consistently outperforms 7 mainstream RL
algorithms. With just 1K RL samples, Qwen3-14B with AEPO achieves impressive results: 47.6% on
GAIA, 11.2% on Humanity's Last Exam, and 43.0% on WebWalker for Pass@1; 65.0% on
GAIA, 26.0% on Humanity's Last Exam, and 70.0% on WebWalker for Pass@5. Further analysis reveals that AEPO improves rollout sampling diversity while maintaining stable policy
entropy, facilitating scalable web
agent training.
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All authors Guanting Dong, Licheng Bao, Zhongyuan Wang, Kangzhi Zhao, Xiaoxi Li, Jiajie Jin, Jinghan Yang, Hangyu Mao, Fuzheng Zhang, Kun Gai, Guorui Zhou, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
Computation and Language · Cross-listed from cs.SE
E2Edev: Benchmarking Large Language Models in End-to-End Software Development Task
Jingyao Liu, Chen Huang, Zhizhao Guan et al. (+2 more)
The rapid advancement in large
language models (LLMs) has demonstrated significant potential in End-to-End Software Development (E2ESD). However, existing E2ESD
benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols, hindering a true understanding of current framework capabilities. To address these limitations, we present E2EDev, a novel
benchmark grounded in the principles of Behavior-Driven Development (BDD), which evaluates the capabilities of E2ESD frameworks by assessing whether the generated software meets user needs through mimicking real user interactions (Figure 1). E2EDev comprises (i) a fine-grained set of user requirements, (ii) multiple BDD test scenarios with corresponding Python step implementations for each requirement, and (iii) a fully automated testing pipeline built on the Behave framework. To ensure its quality while reducing the annotation effort, E2EDev leverages our proposed Human-in-the-Loop Multi-
Agent Annotation Framework (HITL-MAA). By evaluating various E2ESD frameworks and LLM backbones with E2EDev, our analysis reveals a persistent struggle to effectively solve these tasks, underscoring the critical need for more effective and cost-efficient E2ESD solutions. Our codebase and
benchmark are publicly available at https://github.com/SCUNLP/E2EDev.
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All authors Jingyao Liu, Chen Huang, Zhizhao Guan, Wenqiang Lei, Yang Deng
Computation and Language
Efficient Seq2seq Coreference Resolution Using Entity Representations
Matt Grenander, Shay B. Cohen and Mark Steedman
Seq2seq coreference models have introduced a new paradigm for coreference resolution by learning to generate text corresponding to coreference labels, without requiring task-specific parameters. While these models achieve new state-of-the-art performance, they do so at the cost of flexibility and efficiency. In particular, they do not efficiently handle incremental settings such as dialogue, where text must processed sequentially. We propose a compressed representation in order to improve the efficiency of these methods in incremental settings. Our method works by extracting and re-organizing entity-level
tokens, and discarding the majority of other input
tokens. On OntoNotes, our best model achieves just 0.6 CoNLL F1 points below a full-prefix, incremental baseline while achieving a compression ratio of 1.8. On LitBank, where singleton mentions are annotated, it passes state-of-the-art performance. Our results indicate that discarding a wide portion of
tokens in seq2seq resolvers is a feasible strategy for incremental coreference resolution.
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Computation and Language
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
Haolin Li, Haipeng Zhang, Mang Li et al. (+4 more)
Large
language models (LLMs) continue to struggle with low-resource
languages, primarily due to limited training data, translation noise, and unstable cross-lingual
alignment. To address these challenges, we propose LiRA (Linguistic Robust Anchoring for LLMs)-a plug-and-play framework that requires only lightweight fine-tuning on top of existing pretrained backbones. LiRA jointly optimizes representation stability and cross-lingual semantic consistency by combining two key components: Arca (Anchored Representation Composition Architecture), which aligns low-resource inputs to a shared English semantic space through anchor-based
alignment and collaborative encoding; and LaSR (
Language-coupled Semantic Reasoner), a lightweight,
language-aware head that enforces consistency regularization for unified cross-lingual understanding,
retrieval, and
reasoning. We theoretically show that under controlled anchoring error and translation-induced bias, LiRA guarantees bounded representation deviation and stable downstream performance under local Lipschitz continuity. To facilitate research, we release a new
multilingual product
retrieval dataset covering five Southeast Asian and two South Asian
languages. Extensive experiments across diverse low-resource
benchmarks demonstrate consistent improvements in
retrieval, ranking, question answering, and
reasoning tasks. Code will be publicly available on GitHub, and the
dataset will be hosted on Hugging Face.
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All authors Haolin Li, Haipeng Zhang, Mang Li, Yaohua Wang, Lijie Wen, Yu Zhang, Biqing Huang