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Wei Liu, Li Yang, Yufei Wang, Han Xiao, Boyu Cai, Weiming Hu

Spiking Neural Networks (SNNs) naturally process visual inputs across multiple timesteps, offering rich temporal dynamics and energy-efficient computation. However, the temporally invariant supervision commonly used in training tends to reinforce the same dominant response patterns across timesteps, leading to redundant representations and limiting temporal discriminability. To overcome this limitation, we introduce Temporal Representation Enhancement(TRE), a novel learning-to-forget paradigm that encourages more diverse and complementary temporal representations. TRE identifies high-contribution semantic patterns through class-specific contribution estimation and temporal accumulation, and selectively suppresses them using a dynamic modulation strategy. By redirecting the model's attention toward alternative yet informative semantic cues, TRE promotes the learning of complementary features across timesteps. This approach not only strengthens the temporal discriminative capacity of SNNs but also enables more effective multi-timestep learning by leveraging richer semantic information. Extensive experiments on both static image datasets and dynamic neuromorphic datasets demonstrate that TRE consistently improves classification accuracy and feature diversity across different SNN backbones.

Sanaz Karimijafarbigloo, Armin Khosravi, Alireza Kheyrkhah, Reza Azad, Mauricio Reyes, Dorit Merhof

Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing approaches often reduce this diversity to consensus labels or treat rater differences as noise, resulting in overconfident and poorly calibrated models. We propose a harmonized probabilistic framework that disentangles acquisition artifacts from genuine annotator variability through adaptive feature conditioning and frequency-domain personalization. A lightweight Harmonizer Network implicitly models scanner-specific artifacts and performs dynamic feature modulation to standardize latent representations, ensuring that uncertainty reflects anatomy rather than noise. To represent rater-specific styles, we introduce High-Frequency Prompt Modules that operate in the spectral domain to encode annotator-dependent boundary precision and textural sensitivity. These prompts adaptively modulate harmonized features to produce personalized yet anatomically consistent segmentations. Furthermore, a Generalized Energy Distance (GED)-based regularization aligns the generative distribution with empirical annotation variability, promoting diversity where experts disagree and consensus where they converge. Experiments on LIDC-IDRI and NPC-170 show SOTA aggregated and individualized segmentation, with notable GED reductions and improved Dice scores, especially on noisy cases. Beyond accuracy, the model exhibits clinically meaningful uncertainty, confidence rises in agreement regions and declines in ambiguous areas, supporting its use as a reliable and interpretable tool for multi-expert clinical workflows.

Ruiyang Li, Fang Liu, Licheng Jiao, Xinglin Xie, Jiayao Hao, Shuo Li, Xu Liu, Jingyi Yang, Lingling Li, Puhua Chen 等

Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer from acquisition noise and annotation ambiguity, causing pervasive data uncertainty that substantially undermines model robustness. Existing research focuses primarily on model architectural improvements and predictive reliability estimation, while systematic exploration of the intrinsic data uncertainty remains insufficient. To address this gap, this work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty. Specifically, we analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class, thereby measuring sample difficulty and aleatoric uncertainty. Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability. Experimental results on five public datasets encompassing CT and MRI modalities and involving multi-organ and tumor segmentation tasks demonstrate that our method achieves significant and robust performance improvements across various mainstream network architectures, revealing the broad application potential of aleatoric uncertainty in medical image understanding and segmentation tasks. The code is available.

Jiajie Zhang, Sören Schwertfeger, Alexander Kleiner

We present a novel unsupervised framework to unlock vast unlabeled human demonstration data from continuous industrial video streams for Vision-Language-Action (VLA) model pre-training. Our method first trains a lightweight motion tokenizer to encode motion dynamics, then employs an unsupervised action segmenter leveraging a novel "Latent Action Energy" metric to discover and segment semantically coherent action primitives. The pipeline outputs both segmented video clips and their corresponding latent action sequences, providing structured data directly suitable for VLA pre-training. Evaluations on public benchmarks and a proprietary electric motor assembly dataset demonstrate effective segmentation of key tasks performed by humans at workstations. Further clustering and quantitative assessment via a Vision-Language Model confirm the semantic coherence of the discovered action primitives. To our knowledge, this is the first fully automated end-to-end system for extracting and organizing VLA pre-training data from unstructured industrial videos, offering a scalable solution for embodied AI integration in manufacturing.

Shivanshu Shekhar, Uttaran Bhattacharya, Raghavendra Addanki, Mehrab Tanjim, Somdeb Sarkhel, Tong Zhang

Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle to capture subtle temporal dynamics. We propose a fundamentally different approach: repurposing video generative models, which are inherently designed to model temporal structure, as reward models. We present the Generative-Transformer-based Self-Supervised Video Judge (\modelname), a novel evaluation model that transforms state-of-the-art video generation models into powerful temporally-aware reward models. Our key insight is that generative models can be reformulated as energy-based models (EBMs) that assign low energy to high-quality videos and high energy to degraded ones, enabling them to discriminate video quality with remarkable precision when trained via contrastive objectives. To prevent the model from exploiting superficial differences between real and generated videos, we design challenging synthetic negative videos through controlled latent-space perturbations: temporal slicing, feature swapping, and frame shuffling, which simulate realistic but subtle visual degradations. This forces the model to learn meaningful spatiotemporal features rather than trivial artifacts. \modelname achieves state-of-the-art performance on GenAI-Bench and MonteBench using only 30K human-annotations: 6xto 65xfewer than existing VLM-based approaches.

Tarun Kathuria, Sachin Kumar

We present a discrete diffusion-based generative model for text generation using Glauber dynamics from statistical physics. Our main insight is that instead of trying to train a discrete state space diffusion model using Glauber dynamics with a uniform transition kernel as the forward process, one can set up an “energy function” based on pretrained causal/masked language models, which, when viewed as the stationary distribution, allows us to significantly improve the quality of the generated text. Using UL2 as our pretrained models and modifying and incorporating it into our diffusion pipeline, we obtain significantly better perplexities than prior diffusion-based text generative models and are competitive with the perplexities of GPT-2-medium and GPT-2-large for comparable model sizes. Furthermore, our models outperform prior diffusion models and GPT-2 style auto-regressive models on some zero-shot common sense reasoning tasks as well as some planning/search tasks.

Seyedarmin Azizi, Erfan Baghaei Potraghloo, Souvik Kundu, Massoud Pedram

Large language models (LLMs) demonstrate strong performance on multi-step reasoning tasks by producing intermediate explanations, commonly referred to as chains of thought (CoTs). However, the generated rationales are typically verbose, consuming many additional tokens, and thus degrading throughput and increasing inference energy consumption. Interestingly, we find that verbose and concise CoTs correspond to distinct regions in the model’s intermediate activation space, suggesting that verbosity is a steerable latent attribute. Building on this observation, we develop an inference-time method to automatically steer the model response towards concise reasoning traces without updating model parameters. Our method, dubbed _ASC_ (Activation-Steered Compression), generates concise CoTs by directly adjusting internal representations via activation steering. A key component of ASC is **Contrastive Energy-Based Steering (CES)**, a principled procedure to learn a _single_ steering vector from a small set of verbose–concise CoT pairs by optimizing a length-normalized contrastive energy objective. To further ensure reliable steering and preserve general utility, CES enforces a differentiable **KL trust region** during steering vector optimization, explicitly constraining the distribution shift within a specified budget. With only 100 pairs of verbose–concise examples, ASC reduces the generated token length by as much as 69.4% across five reasoning benchmarks (MATH500, GSM8K, LiveCodeBench, GSM8K-Hard, and AQuA-RAT) while maintaining accuracy across models with 1.5B, 7B, 8B, and 32B parameters. On MATH500, ASC achieves an end-to-end inference speed-up of 2.7× on an 8B model.

Michael S. Yantosca, Albert M. K. Cheng

Recent automated transcription systems have focused on end-to-end orthographic approaches driven by deep neural networks and sequence-to-sequence transformers. Growing public interest in transcription at the phonemic or phonetic level has led to re-purposing these systems to segment and identify phones, the basic sounds which comprise human speech. However, they miss the mark on a fundamental component of time-series analysis, namely time. For linguistic applications which require high fidelity in the temporal domain, the loss of timing information is untenable. Our work proposes a deadline-bounded expectation maximization (EM) algorithm with a novel initialization method to estimate formants, i.e., salient speech frequencies, for enhanced phonetic segmentation. Based on the concept of spectral gravity, i.e., treating spectral energy as mass attenuated by the square of frequency distance across the spectrum, our technique outperforms the recent state of the art on key clustering metrics, generating reasonable alignments across multiple languages with no a priori training.

Nupoor Gandhi, Michael Bada, Emma Strubell

In specialized domains that require expert annotators and high inter-annotator agreement, high-quality datasets with span-bound semantic concept annotations remain expensive to develop. Substantial resources are typically spent on \textit{unitizing}, the task of identifying precise span boundaries for entity mentions. Unitizing is a significant source of inter-annotator disagreement, a poor use of expensive domain expertise, and very time-consuming. We propose a lighter annotation procedure that concentrates manual efforts on typed position annotations, marking positions in the text that overlap with mentions of each entity type, abstracting away span boundary decisions. With as few as 100-200 example sentences, we train span boundary detection models to unitize typed position annotations. Through evaluation over three datasets: CRAFT (biomedical), GENIA (molecular biology), and POLIANNA (climate/energy policy text), we demonstrate that (1) annotating typed positions in the text instead of full concept annotation is a more efficient use of time in low-resource settings, and (2) model-inferred span boundaries result in higher agreement at both the annotator training and corpus annotation phases, without sacrificing utility.

Danyu Huang, Yao Zhang, Jun Wang, Zhenglu Yang

Large Language Models have achieved impressive results in general reasoning tasks. However, they still face significant challenges when applied to temporal knowledge graph question answering (TKGQA), particularly exhibiting broken temporal reasoning chains and a lack of dynamic error-correction. These limitations hinder their capacity to handle complex temporal logic and make it difficult to recover once a reasoning error occurs. To address this issue, we propose Regret-Now, a novel LLM-based temporal reasoning framework inspired by the physical principle of minimum potential energy. Regret-Now models the reasoning process as a dynamic trajectory moving toward a more stable state, where each step is expected to a lower potential energy. We introduce the Regret Stage that evaluates the “potential energy” of each intermediate reasoning step and triggers real-time rollback if an abnormal rise in potential energy is detected—indicating a likely error. We evaluate Regret-Now on two standard TKGQA benchmarks: CronQuestions and MultiTQ. Experimental results show consistent gains over strong baselines, validating physics-inspired modeling for LLM-based TKGQA. The code can be found at https://github.com/h-yii/Regret-Now.

Geyuan Zhang, Xiaofei Zhou, Shihao Liu, Jingyuan Tian, Jizheng Ma

Knowledge forgetting is a central challenge when adapting LLMs to new tasks. Prior studies indicate that pretrained knowledge is concentrated in the principal singular subspace of pretrained weight W_0; so recent Low-Rank Adaptation (LoRA) variants initialize LoRA in the minor subspace to steer early updates away from principal directions and mitigate forgetting. However, we observe that during fine-tuning, the update direction progressively shifts from the minor to the principal subspace, which is called as Singular-subspace Drift (SD), thereby allocating more energy to the directions that carry pretrained knowledge and leaving a persistent risk of forgetting. To address this issue, we propose Singular-subspace Drift Controlled LoRA (SDC-LoRA), which constrains the growth of update energy in the principal singular subspace of W_0 and thus mitigate SD. SDC-LoRA proposes Principal Subspace Energy-Controlled Learning, using Spectral Calibration factor \gamma_{\mathrm{sc}} to selectively downscale gradients along the principal singular subspace of W_0 while keeping minor-subspace updates unchanged. Across extensive experiments with LLaMA-3.1-8B-Instruct and Qwen2.5-7B-Chat on MetaMathQA and CodeFeedback, SDC-LoRA mitigates forgetting on MMLU, TruthfulQA, and HellaSwag while matching or improving GSM8K and HumanEval, offering a practical route to adapt LLMs without sacrificing prior knowledge.

Long Li, Zhijian Zhou, Jiangxuan Long, Peiyang Liu, Weidi Xu, Zhe Wang, Shirui Pan, Chao Qu

Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck is the credit assignment problem. In traditional paradigms, rewards are determined solely by the final-turn feedback, which ignores the intermediate process and leads to ambiguous credit evaluation. To address this, we propose Agentic SQL, a framework featuring a universal two-tiered reward mechanism designed to provide effective trajectory-level evaluation and dense step-level signals. First, we introduce Aggregated Trajectory Reward (ATR) to resolve multi-turn credit assignment. Using an asymmetric transition matrix, ATR aggregates process-oriented scores to incentivize continuous improvement. Leveraging Lyapunov stability theory, we prove ATR acts as an energy dissipation operator, guaranteeing a cycle-free policy and monotonic convergence. Second, Column-Set Matching Reward (CSMR) provides immediate step-level rewards to mitigate sparsity. By executing queries at each turn, CSMR converts binary (0/1) feedback into dense [0,1] signals based on partial correctness. Evaluations on BIRD show a 5% gain over binary-reward GRPO. Notably, our approach outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models, propelling Text-to-SQL toward a robust multi-turn agent paradigm.

He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong

Large language models with billions of parameters are often over-provisioned: many layers contribute little unique information yet dominate the memory and energy footprint during inference. We present LieQ (Layer-wise information effectiveness Quantization), a hardware-native, metric-driven post-training quantization framework that addresses the critical challenge of maintaining accuracy in sub-8B models, model parameters less than 8B, under extreme low-bit compression. LieQ keeps uniform bit-width within each layer while mixing precision across layers, preserving standard multiplication kernels and avoiding irregular memory access, codebooks, or irregular formats at inference time. Our method uncovers a strong correlation between layer-wise functional saliency and representational compactness, revealing that layers with higher training-induced energy concentration are functionally irreplaceable. Leveraging this insight, we propose a purely geometry-driven sensitivity proxy that enables automatic bit-width allocation under a target average-bit budget without expensive gradient updates or inference-based perplexity probing. Under an average weight bit-width approaching two bits per parameter, LieQ consistently reduces the large accuracy gap typically observed for naive uniform 2-bit baselines on Qwen3 and LLaMA3.x families, while retaining standard-kernel efficiency. These properties make LieQ a practical path toward deploying small language models on resource-constrained edge devices. Code will be available at: https://github.com/HeXiao-55/LieQ-official.git.

Haitao Li, Chunxiang Jin, Chenglin Li, Wenhao Guan, Zhengxing Huang, Xie Chen

Zero-shot text-to-speech models can clone a speaker’s timbre from a short reference audio, but they also strongly inherit the speaking style present in the reference. As a result, synthesizing speech with a desired style often requires carefully selecting reference audio, which is impractical when only limited or mismatched references are available. While recent controllable TTS methods attempt to address this issue, they typically rely on absolute style targets and discrete textual prompts, and therefore do not support continuous and reference-relative style control. We propose ReStyle-TTS, a framework that enables continuous and reference-relative style control in zero-shot TTS. Our key insight is that effective style control requires first reducing the model’s implicit dependence on reference style before introducing explicit control mechanisms. To this end, we introduce Decoupled Classifier-Free Guidance (DCFG), which independently controls text and reference guidance, reducing reliance on reference style while preserving text fidelity. On top of this, we apply style-specific LoRAs together with Orthogonal LoRA Fusion to enable continuous and disentangled multi-attribute control, and introduce a Timbre Consistency Optimization module to mitigate timbre drift caused by weakened reference guidance. Experiments show that ReStyle-TTS enables user-friendly, continuous, and relative control over pitch, energy, and multiple emotions while maintaining intelligibility and speaker timbre, and performs robustly in challenging mismatched reference–target style scenarios. Code and data are available in supplementary materials.

Jiale Chang, Ying Li, Siliang Tang, Yueting Zhuang

Multimodal large language models excel at vision-language tasks but remain prone to hallucinations that undermine their reliability. Existing approaches predominantly treat hallucinations as classification errors, overlooking the heterogeneous behaviors of attention heads and their dynamic influences during inference. We revisit MLLM reasoning from an energy perspective and identify that hallucinations stem from imbalances between visual potential and language prior potential: when visual information is ambiguous or language priors dominate, attention heads tend to be driven by linguistic statistical patterns, generating content inconsistent with visual evidence. We propose AHEAD, a framework that quantifies the energetic properties of each attention head during object generation through two potential networks—the Visual Grounding Potential Network and the Language Prior Potential Network—and dynamically adjusts their contributions at inference time. Specifically, we amplify attention heads with strong visual grounding capacity while suppressing those overly reliant on language priors. Experiments across multiple benchmarks demonstrate that AHEAD significantly reduces hallucination rates without fine-tuning the base MLLM while maintaining generation quality.

Guan Huang, Tao Shu

In deployed large language models (LLMs), inference energy consumption has grown rapidly and has emerged as a key bottleneck in large-scale deployment, yet most existing inference efficiency methods focus on reducing FLOPs or latency, rather than explicitly modeling or enforcing end-to-end inference energy constraints. We propose EOP-LLM, an energy-oriented dynamic pruning framework that enables LLM inference under explicit per-sequence energy budgets. EOP-LLM combines a device-calibrated energy proxy with lightweight token and feed-forward (FFN) selectors, coordinated through a global dual variable, to dynamically allocate computation while preserving model quality. Extensive experiments on LLaMA 3.2 (1B/3B) and LLaMA 3.1 (8B) demonstrate that EOP-LLM consistently outperforms state-of-the-art dynamic pruning baselines under matched energy budgets, while strictly adhering to per-sequence energy constraints.

Shuoyang Sun, Jiaxin Hong, Yv Zhang, Kuofeng Gao, Hao Fang, Fan Mo, Bin Chen, Shu-Tao Xia

3D Vision-Language Models (3D-VLMs) have emerged as the critical cognitive backbone for spatial intelligence, enabling precise reasoning over unstructured 3D data. While these models serve as the foundation for downstream robotics and embodied systems, their reliance on autoregressive decoding introduces a fundamental vulnerability regarding inference efficiency. In this work, we present \textbf{Inflate3D}, a novel adversarial framework designed to trigger computational and economic exhaustion in 3D-VLMs. Specifically, we exploit the model’s sensitivity to untrusted 3D assets to hijack the generation process. Inflate3D operates by injecting imperceptible noise that forces the model into a state of pathological verbosity, effectively stalling the inference pipeline. Our approach comprises two synergistic strategies: (1) a \textit{semantic-aware adversarial manipulation} that leverages internal representations to selectively perturb semantically critical regions while preserving geometric structure, and (2) a \textit{trajectory disruption mechanism} that manipulates token probabilities to suppress End-of-Sequence (EOS) emission, thereby prolonging decoding and inducing verbose outputs. Experiments on standard benchmarks show that Inflate3D amplifies output length and energy consumption by up to \textbf{6.45$\times$}, demonstrating a potent capability to drain system resources. These findings expose a critical blind spot in multimodal alignment, highlighting the urgent need to secure spatial foundation models against resource exhaustion attacks.

Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang 等

The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilities. This limitation hinders the model’s ability to follow text instructions for controllable Text-to-Speech (TTS). To address this, we propose a new paradigm inspired by operationalism that decouples instruction understanding from speech generation. We introduce BatonVoice, a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan – explicit vocal features (e.g., pitch, energy). A separate TTS model, the orchestra, then generates the speech from these features. To realize this component, we develop BatonTTS, a TTS model trained specifically for this task. Our experiments demonstrate that BatonVoice achieves strong performance in controllable and emotional speech synthesis, outperforming strong open- and closed-source baselines. Notably, our approach enables remarkable zero-shot cross-lingual generalization, accurately applying feature control abilities to languages unseen during post-training. This demonstrates that objectifying speech into textual vocal features can more effectively unlock the linguistic intelligence of LLMs.

Yijia Fan, Jing Yang, Mingyu Liu, Kaitong Cai, Jian Wang, Keze Wang, Jusheng Zhang

Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm for text generation, offering parallel decoding and bidirectional context modeling. However, aligning dLLMs with reinforcement learning (RL) remains a significant challenge, as the marginal likelihood of sequences in masked diffusion is typically intractable, rendering standard policy gradient methods unstable or computationally prohibitive. In this work, we propose **Diffusion-Gibbs Alignment (DGA)**, a novel variational framework that reformulates RL for dLLMs as a distribution matching problem. DGA bypasses the explicit computation of log-probabilities by leveraging a learned energy function to model the relative quality of samples. The optimization is decoupled into two stable steps: (1) contrastive energy ranking to capture global reward structures, and (2) weighted diffusion alignment to update the policy via importance sampling. Empirically, DGA establishes a new state-of-the-art across logical reasoning (Sudoku, Countdown), mathematical reasoning (GSM8K, Math500), and code generation (HumanEval, MBPP) benchmarks. DGA offers a novel variational perspective for dLLM alignment, achieving better performance while simultaneously enhancing training speed and memory efficiency.

Yilong Chen, Junyuan Shang, Yuchen Feng, Zhenyu Zhang, Naibin Gu, Ziqi Wang, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu 等

Mixture-of-Experts (MoE) is a cornerstone for scaling LLMs, yet its training dynamics remain poorly understood, often leading to sub-optimal specialization. Moving beyond static routing, we present a systematic study of the MoE lifecycle using Helmholtz Free Energyand Router Entropy. We identify a universal Three-Stage Phase Transition—Exploration, Symmetry Breaking, and Stabilization—marked by an Energy Climb and Plateau. This reflects Frustrated Exploration, caused by structural interference between specialization drives and uniformity constraints. To address this, we propose Uncertainty-Aware Routing (UAR), which aligns routing with the model’s epistemic state via: (1) Evidence-Triggered Expansion, increasing active experts for high-energy tokens, and (2) Epistemic Masking, applying load-balancing only in high-uncertainty regimes to shield mature experts. Experiments confirm UAR reduces perplexity and improves expert distinctiveness, offering a principled path toward thermodynamically aligned computation.