Generative engine optimization (GEO) has reshaped how sellers compete for visibility in LLM-generated rankings. By embedding persuasive arguments in the content they control, sellers can influence how generative engines rank products and web pages and push their own items to the top. Existing defenses either detect harmful intent or unnatural language, which fluent GEO content often lacks, or rewrite all candidates, which alters legitimate listings without identifying the manipulated ones. However, these approaches overlook the persuasive structure through which GEO attacks influence LLM-based rankings. To address this gap, we present SCI-Defense, a framework for detecting persuasion-structured ranking manipulation, in which an LLM judge scores five persuasion devices grounded in research on influence and deception: authority, social proof, scarcity and urgency, comparative claims, and narrative purposiveness. Two lightweight gates for unnatural strings and inflated relevance complement the judge, and flagged candidates are demoted rather than rewritten, each with a record of the candidate and the criterion that triggered the flag. On 1,152 optimized CORE attacks across six Amazon product categories and six MS MARCO web domains, SCI-Defense detects 89.6% of reasoning attacks and 96.2% of review attacks at a 1.2% false positive rate and blocks 96.1% of the attacks that pushed their target into the top three. On a 120-query subset, paraphrasing and truncation block a similar share of attacks but push 4.0--4.5% of honest items out of the top three, compared with 0.6% for SCI-Defense.
Min Zeng, Xin Wen, Xi Chen, Yichen Zhang, Wen-Xin Zhou
We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated continuation values. The calibration parameter is defined through Bellman orthogonality, recovering the physical parameter under correct simulator specification and yielding a near-optimal $L_2$ approximation of the policy value under misspecification. We propose three TD algorithms based on random-direction finite differences, distinguished by their query costs and reuse of noisy simulator evaluations. We establish almost-sure convergence, last-iterate mean-square error bounds, and central limit theorems for Polyak-Ruppert averages across four query-noise regimes. Our analysis reveals a specification-dependent bias phenomenon: for two algorithms, the finite-difference bias of the TD estimating equation vanishes at the calibration target under correct specification, even when the gradient surrogate remains biased. This permits a fixed perturbation radius and rate exponents approaching $1/2$ in the simulator rollout budget, whereas misspecification generally recovers the classical Kiefer-Wolfowitz rates. We also develop a few-replicate online bootstrap that yields Student-$t$ calibrated confidence intervals under negligible query noise, and identify a covariance mismatch that can invalidate conventional multiplier inference along Markov trajectories. Finally, a second-stage TD recursion corrects the remaining value discrepancy without additional simulator queries.
Post-training quantization (PTQ) has emerged as a promising approach for reducing the memory footprint and computational cost of large language models (LLMs), enabling efficient deployment without full model retraining. However, existing PTQ methods struggle to simultaneously support weight–activation joint quantization and extreme low-bit weight quantization. This limitation primarily arises from the depth of LLMs and their strong cross-layer dependencies, which cause quantization errors to propagate and accumulate across layers, ultimately leading to significant performance degradation. In this paper, we present ACBQ, a simple yet effective framework that simultaneously addresses weight–activation joint quantization and extreme weight quantization. We first propose a granular quantization strategy that treats self-attention and FFN as separate quantization units with module-specific optimization objectives. To mitigate the propagation and accumulation of quantization errors across layers, we introduce an adaptive cross-block quantization strategy that explicitly accounts for cross-layer dependencies by encouraging consistency across blocks. Extensive experiments across diverse LLMs, including OPT and the LLaMA family, demonstrate that ACBQ achieves superior performance under both W4A4 and highly aggressive W2 settings, while incurring negligible additional computational overhead.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Qihua Dong, Yitian Zhang, Huimin Zeng Northeastern University, Yizhou Wang Adobe Systems, Jianglin Lu, Kuo Yang Northeastern University, Yun Fu
Multimodal large language models (MLLMs) are making rapid strides in complex visual reasoning. This survey synthesizes the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT), where models ground intermediate inferences by interleaving textual rationales with visual state updates. We formalize IG-CoT, present a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning, and map these techniques to representative benchmarks. Our analysis identifies two domains where IG-CoT offers significant advantages: detail-oriented reasoning requiring meticulous perception, and imagined-world reasoning for simulating unseen states in games, geometry, and planning. We discuss the practical trade-offs of current methods regarding controllability, data, and compute. We conclude by highlighting key challenges (efficiency, data quality, and generative capabilities) and outlining promising future directions, including lightweight architectures, richer intermediate supervision, and method-aware evaluations that better assess faithfulness and long-horizon reasoning. We maintain a continuously updated paper list at https://github.com/dddraxxx/Awesome-Image-Grounded-CoT.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jiahao Tang Central South University, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao Central South University, Yang Wan, Jingru Tan Central South University 等 11 位作者
We introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models (LMMs). Chart2Code is explicitly designed from a user-driven perspective, capturing diverse real-world scenarios and progressively increasing task difficulty. It consists of three levels: Level 1 (Chart Reproduction) reproduces charts from a reference figure and user query; Level 2 (Chart Editing) involves complex modifications such as changing chart types or adding elements; and Level 3 (Long-Table to Chart Generation) requires models to transform long, unprocessed tables into faithful charts following user instructions. To our knowledge, this is the first hierarchical benchmark that reflects practical chart2code usage while systematically scaling task complexity. In total, Chart2Code contains 2,186 tasks across 22 chart types, paired with multi-level evaluation metrics that assess both code correctness and the visual fidelity of rendered charts. We benchmark 29 state-of-the-art (SoTA) LMMs, including both proprietary and the latest open-source models such as GPT-5.2, Qwen3-VL, InternVL3/3.5, MiMo-VL, and Seed-1.6-VL. Experimental results demonstrate that even the SoTA model GPT-5.2 averages 72.21 on code-based evaluation and only 33.41 on chart-quality assessment across the editing tasks, underscoring the difficulty of Chart2Code. We anticipate this benchmark will drive advances in multimodal reasoning and foster the development of more robust and general-purpose LMMs.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jiaju Chen, Yuxuan Lu, Xiaojie Wang Amazon, Huimin Zeng, Jing Huang Amazon, Jiri Gesi Amazon Science, Ying Xu Harvard University, Harvard University, Bingsheng Yao Northeastern University 等 9 位作者
Nearly all human work is collaborative; thus, the evaluation of real-world NLP applications often requires multiple dimensions that align with diverse human perspectives. As real human evaluator resources are often scarce and costly, the emerging “LLM-as-a-judge” paradigm sheds light on a promising approach to leverage LLM agents to believably simulate human evaluators. Yet, to date, existing LLM-as-a-judge approaches face two limitations: persona descriptions of agents are often arbitrarily designed, and the frameworks are not generalizable to other tasks. To address these challenges, we propose MAJ-EVAL, a Multi-Agent-as-Judge evaluation framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents (e.g., research papers), instantiate LLM agents with the personas, and engage in-group debates with multi-agents to generate multi-dimensional feedback. Our evaluation experiments in both the educational and medical domains demonstrate that MAJ-EVAL can generate evaluation results that better align with human experts’ ratings compared with conventional automated evaluation metrics and existing LLM-as-a-judge methods.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表