Logical reasoning is a core challenge in natural language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical theorem proving, and complex decision-making. Despite the remarkable progress of large language models (LLMs), most current approaches still rely on forward reasoning paradigms, generating step-by-step rationales from premises to conclusions. However, such methods often suffer from redundant inference paths, hallucinated steps, and semantic drift, resulting in inefficient and unreliable reasoning. In this paper, we propose a novel framework, Hypothesis-driven Backward Logical Reasoning (HBLR). The core idea is to integrate confidence-aware symbolic translation with hypothesis-driven backward reasoning. In the translation phase, only high-confidence spans are converted into logical form, such as first-order logic (FOL), while uncertain content remains in natural language. A translation reflection module further ensures semantic fidelity by evaluating symbolic outputs and reverting lossy ones back to text when necessary. In the reasoning phase, HBLR simulates human deductive thinking by assuming the conclusion is true and recursively verifying its premises. A reasoning reflection module further identifies and corrects flawed inference steps, enhancing logical coherence. Extensive experiments on five reasoning benchmarks demonstrate that HBLR consistently outperforms strong baselines in both accuracy and efficiency.

Logical reasoning with large language models (LLMs) has received growing attention. One mainstream approach translates natural language into formal logic and then applies symbolic solvers for deduction. While effective in many tasks, these LLM-based translators often fail to generate consistent symbolic representations when the same concept appears in different linguistic forms. Such inconsistencies break logical coherence and lead to solver errors. However, most existing benchmarks lack this type of linguistic variation, which frequently occurs in real-world text, leaving the problem underexplored. To address this gap, we present SoLT, a benchmark that systematically rewrites reasoning datasets into diverse yet logically equivalent forms across multiple levels. Beyond evaluation, SoLT also provides a general method to enrich any dataset with linguistic diversity while preserving both meaning and logic. To further enhance the stability of LLM-based reasoning, we propose MenTaL, which explicitly guides models to build a concept–symbol mapping table during translation. By linking equivalent expressions to shared symbols, MenTaL maintains consistency and mitigates symbol drift. Experiments on SoLT demonstrate that LLMs indeed suffer from inconsistent symbol mapping under linguistic variation, leading to significant drops in reasoning accuracy. Meanwhile, applying MenTaL brings clear and stable performance improvements across diverse inputs. Overall, our findings reveal that overlooking linguistic diversity hides key weaknesses in LLM-based translators, and our work offers a step toward more reliable logical reasoning in varied real-world scenarios. Our code is available at https://github.com/wufeiwuwoshihua/LinguDiver.

Large Vision-Language Models (LVLMs) achieve remarkable performance in multimodal tasks but suffer from high computational costs due to the large number of visual tokens.Existing pruning methods either apply after visual tokens enter the LLM or perform pre-pruning based solely on visual attention.Both fail to balance efficiency and semantic alignment, as post-pruning incurs redundant computation, while visual-only pre-pruning overlooks multimodal relevance.To address this limitation, we propose Dual-Cue Pruning (DCP), a novel cross-modal pruning framework that jointly considers textual semantics and visual selfattention.DCP consists of a text-aware computation module, which employs a gradientweighted attention mechanism to enhance textvisual alignment, and an image-aware computation module, which utilizes deep-layer selfattention distributions to retain essential structural information.By integrating both cues, DCP adaptively selects the most informative visual tokens, achieving efficient inference acceleration while maintaining strong task performance.Experimental results show that DCP can retain only 25% of the visual tokens, with a minimal performance degradation of 0.063% on LLaVA-1.5-13B,demonstrating its effectiveness in balancing efficiency and accuracy.

Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Large language models (LLMs) have gained considerable attention for their remarkable performance across a wide range of tasks. However, efficient LLM inference remains challenging because of the autoregressive decoding process, which generates only one token at a time. Speculative decoding has been introduced to address the limitation by using small speculative models (SSMs) to speed up LLM inference. However, the low acceptance rate of SSMs and the high verification cost of LLM prohibit further performance improvement. In this paper, we present Smurfs, an LLM inference system designed to accelerate LLM inference through collective and adaptive speculative decoding. Smurfs adopts a majority-voted mechanism that harnesses multiple SSMs to collaboratively predict LLM outputs in multi-task scenarios, while avoiding high verification cost. It also decouples SSM speculation from LLM verification and uses a pipelined execution to hide the latency of SSM speculation. Additionally, Smurfs proposes a mechanism to dynamically determine the optimal speculation length of SSM at runtime, balancing the performance impact of accepted tokens and verification cost. The experimental results demonstrate the superiority of Smurfs in terms of inference throughput and latency compared to the state-of-the-art LLM inference systems.

The popularity of recommendation models and the enhanced AI processing capability of CPUs have provided massive performance opportunities to deliver satisfactory experiences to a large number of users. Unfortunately, existing recommendation model training methods fail to achieve high efficiency due to unique challenges such as dynamic shape and high parallelism. To address the above limitations, we comprehensively study the distinctive characteristics of recommendation models and discover several unexploited optimization opportunities. To exploit such opportunities, we proposeAtRec, a high-performant recommendation model training engine that significantly accelerates the training process on CPUs. Specifically,AtRecpresents comprehensive approach of training that employs operator-level and graph-level joint optimizations and runtime optimization. At the operator-level,AtRecidentifies and optimizes the time-consuming operators, which enables further efficient graph-level optimizations. At the graph-level,AtRecconducts an in-depth analysis of the inefficiencies in several frequently used subgraphs, enables further performance improvement via eliminating redundant computations and memory accesses. In addition, to achieve better runtime performance,AtRecalso identifies inefficiencies prevalent in the current scheduling and proposes runtime batching. The experiment results demonstrate thatAtReccan significantly outperform state-of-the-art recommendation model training engines. We have open sourced the implementation and corresponding data ofAtRecto boost research in this direction.

We present FleetRec, a high-performance and scalable recommendation inference system within tight latency constraints. FleetRec takes advantage of heterogeneous hardware including GPUs and the latest FPGAs equipped with high-bandwidth memory. By disaggregating computation and memory to different types of hardware and bridging their connections by high-speed network, FleetRec gains the best of both worlds, and can naturally scale out by adding nodes to the cluster. Experiments on three production models up to 114 GB show that FleetRec outperforms optimized CPU baseline by more than one order of magnitude in terms of throughput while achieving significantly lower latency.