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IJCAI 2026

Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment

Yan Gao, Yazheng Yang, Zhibin Lan, Yidong Chen, Min Zhang, Daimeng Wei, Derek F. Wong, Jinsong Su

Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to the complexity of semantic modeling and the scarcity of CS data. Previous studies mainly rely on the models themselves to implicitly learn semantic representations and resort to costly manual annotations. To mitigate these limitations, we propose enhancing Large Language Models (LLMs) with a Mixture-of-Experts (MoE) speech projector composed of language expert groups, where each group specializes in the semantic space of a specific language for fine-grained speech feature modeling. A language-specific loss and an intra-group load balancing loss are jointly introduced to guide efficient token routing across and within expert groups. Furthermore, we introduce a multi-stage training paradigm that utilizes readily available automatic speech recognition (ASR) and monolingual ST data, facilitating speech-text alignment and improving translation performance. To bridge the data gap for smooth domain transfer, a transition loss is employed to improve adaptation to CS scenarios. Extensive experiments on widely used datasets demonstrate the effectiveness and generality of our approach, achieving average improvements of 0.86 BLEU and 0.93 COMET over SeamlessM4T, with maximum improvements of 1.49 BLEU and 1.41 COMET across different test sets. Our code and supplementary appendices are available at https://github.com/XMUDeepLIT/CSST-SSA.

IJCAI 2026

Streamlining Long-Chain Reasoning via Differentiable Hierarchical Fusion

Chuangen Gao, Wenlun Zhang, Shang Wang, Shuyang Gu

While large-scale reasoning models have achieved remarkable performance gains by scaling test-time computation through extended Chain-of-Thought (CoT) sequences, their practical utility is severely constrained by protracted inference lengths and high computational latency. In this paper, we present Differentiable Hierarchical Fusion (DHF), a novel framework that merges reasoning models with efficient base models via differentiable optimization to produce concise, accurate outputs. We introduce a dual-factor adaptive weighting mechanism to capture intra-block (Attention vs. MLP) variance and inter-block (shallow vs. deep layers) importance hierarchies, thereby addressing key limitations of static merging heuristics. Specifically, DHF optimizes fusion coefficients using gradient descent on a loss function that jointly minimizes cross-entropy (for accuracy) and response length (for conciseness). Furthermore, we enhance an outlier-aware initialization strategy to seed coefficients based on activation density and construct multi-calibration datasets to improve the model’s generalization ability. Comprehensive evaluations on Qwen and LLaMA models across six reasoning benchmarks show that DHF reduces the average response length by 55% while boosting accuracy by 2.8%–6% compared to existing state-of-the-art merging methods.

IJCAI 2026

EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization

Xinbang Dai, Zheyu Xin, Huikang Hu, Lin Ren, Rihui Jin, Guohui Xiao, Kuicai Dong, Zhaocheng Du, Yuyang Zhang

Large Reasoning Models (LRMs) often suffer from overthinking due to redundant verification steps. Existing approaches for mitigating overthinking, such as fast-slow thinking switching and reasoning trajectory compression, fail to make a fine-grained distinction between beneficial and redundant steps within the LRM's reasoning process, and may thus impair reasoning capability in their pursuit of efficiency. To simultaneously improve reasoning efficiency and capability, we propose EvoThink, a framework that reduces redundant verification and encourages the exploration of new reasoning paths. EvoThink comprises two key components: Self-Pruning Training (SPT), an unsupervised method that iteratively prunes redundant reasoning steps and self-trains on the concise trajectories; and Aha-Moment Preference Optimization (AMPO), which, inspired by genetic algorithms, identifies valuable failed reasoning attempts, synthesizes from-wrong-to-right aha-moment data, and optimizes the model to internalize this reasoning pattern. Extensive evaluations across mathematical reasoning and code generation benchmarks demonstrate that EvoThink not only substantially reduces inference-time token usage but also improves the reasoning capability of LRMs.

IJCAI 2026

Subword Tokenization for Low- and Medium-Resource Languages: A Systematic Evaluation

Jón Daðason, Hrafn Loftsson

Subword tokenization is a standard technique for pre-trained language models, mapping text into sequences of tokens from a fixed-size vocabulary. Despite its widespread use, the impact of tokenization algorithms and vocabulary sizes on downstream performance remains underexplored, particularly for low- and medium-resource languages, where suboptimal configurations are difficult to compensate with additional data. Prior studies have mainly focused on high-resource languages, individual tokenization algorithms, or fixed vocabulary sizes, limiting their scope. In this paper, we present a systematic evaluation of subword tokenization strategies in six diverse low- and medium-resource languages: Icelandic, Estonian, Basque, Galician, Nepali, and Tajik. We pre-trained monolingual TEAMS-Small models using WordPiece, BPE, and Unigram tokenizers with vocabulary sizes of 16k, 32k, and 64k, and additionally compared byte-level and character-level tokenization for Icelandic. Evaluation on a benchmark of NLP tasks revealed that Unigram tokenization with a 64k vocabulary consistently outperformed other configurations, with vocabulary size having a greater impact on downstream performance than algorithm choice. These gains were task-dependent, with statistically significant improvements for part-of-speech tagging, named entity recognition, and question answering, but not for dependency parsing and summarization. Finally, byte-level tokenization provided no measurable advantage in a monolingual setting, suggesting that its benefits are primarily relevant for multilingual models.

IJCAI 2026

Interaction Effects in Hybrid Compression of Small Language Models

Iheb Bouriel, Qassim Nasir, Manar Abu Talib

We investigate the interaction between quantization and pruning in the compression of open-weight small language models (SLMs), as these techniques are frequently combined in practice without a clear understanding of whether their effects are antagonistic, synergistic or additive. Prior work has reported ordering effects and studied pruning or quantization in isolation, but rarely quantifies their interaction across models and evaluation metrics. We introduce an interaction coefficient that isolates non-additive effects and apply it to Falcon3-1B-Base and LLaMA-3.2-1B, with a limited study on Qwen2.5-1.5B. Our experiments cover four hybrid application orders, multiple pruning sparsities and 4/8-bit quantization. We find non-monotonic interactions: memory savings are dominated by 4-bit quantization, while pruning alone provides limited benefits. Hybrids exhibit the greatest effects at global unstructured pruning sparsity s ∈ {0.2, 0.3} while getting close to additivity at s=0.5. At low sparsity, truthfulness improves, while reasoning losses diminish at higher sparsity. Future work will extend interaction analysis to hardware-aware sparse kernels and larger models.

IJCAI 2026

PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics

Hao Zhou, Rui Zhang, Han Wan, Hao Sun

Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that struggle with limited measurements and uncertainty quantification. Generative models, by learning distributions over spatiotemporal fields, can better handle sparsity and uncertainty. However, existing generative approaches enforce data consistency and PDE constraints simultaneously via sampling-time gradient guidance, resulting in slow and unstable inference. To this end, we propose PerFlow, a Physics-embedded rectified Flow for efficient sparse reconstruction and uncertainty quantification of spatiotemporal dynamics. PerFlow decouples observation conditioning from physics enforcement, performing guidance-free conditioning by feeding observations into rectified-flow dynamics while embedding hard physics via a constraint-preserving projection (e.g., incompressibility or conservation). Theoretically, we establish invariance guarantees to ensure that trajectories remain on the physics-consistent manifold throughout sampling. Experiments on various PDE systems demonstrate competitive reconstruction accuracy with sound physics consistency, while enabling efficient conditional sampling (e.g., 50 steps) and up to 320x faster inference than 2000-step guided diffusion baselines.

IJCAI 2026

FossilWriter: Learning Hypergraph World Models with Latent Narratives for Creative Story Generation

Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Tianyi Zhang, Jing Liu, Jin Huang

Creative story generation has achieved notable progress with large language models. Current methods construct narratives through hierarchical planning or incremental expansion. These approaches produce structurally complete stories but offer limited support for organic narrative development. Many fiction writers offer a complementary view of the creative process. They often describe their work as gradually uncovering a story embedded in the world they have built. This perspective motivates our approach. We propose that a world model can grow to contain latent narratives as it develops. When characters hold incomplete knowledge and situations remain unresolved, the world structure itself accumulates seeds for story development. Based on this insight we present FossilWriter, a framework that learns hypergraph world models for creative story generation. FossilWriter represents the story world as a shared hypergraph and introduces a narrative layer that marks elements with developmental potential. An excavation module detects and scores latent narratives on this structure. A scene controller selects locations and characters to develop promising narrative threads. The hypergraph grows during writing and serves as the single source of truth to ensure long-range consistency. Experiments on three benchmarks show that FossilWriter achieves an 81.4% win rate on plot coherence and 84.7% on world consistency. Human assessment shows a 32.5% reduction in long-range factual conflicts compared with strong baselines.

IJCAI 2026

MultiGeo: Predicting Drug-Target Affinity via Adaptive Multi-Conformation Ensemble Learning

Ruida Zeng, Cheng Guo, Yajie Meng, Xunkun Cheng, Zhiwei Xu, Xiangzheng Fu, Pan Zeng, Shuting Jin, Junlin Xu

Predicting drug–target affinity (DTA) is central to drug discovery, yet most deep learning models rely on a single static protein structure, neglecting the conformational heterogeneity that underlies many binding mechanisms. We propose MultiGeo, a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot. For each target, MultiGeo starts from an ensemble of structure predictions and uses confidence scores to adaptively select a small set of high-quality, diverse conformers. A hierarchical structural encoder then extracts multi-scale geometric features from these conformers, which are aggregated by a GRU to obtain an ensemble-level representation. To avoid indiscriminately mixing noisy or redundant views, we introduce a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary information. Finally, ligand–target interactions are modeled via a block-wise cross-attention module that captures multi-perspective dependencies between ligand and protein features. Extensive experiments on multiple DTA benchmarks demonstrate that MultiGeo consistently outperforms state-of-the-art baselines, showing that explicitly encoding conformational diversity yields more accurate and robust affinity prediction.

IJCAI 2026

Mapping the Efficiency Landscape of Small Language Models

Fabian Reichwald, Lukas Schiesser, Christiane Plociennik, Leonhard Kunz, Simon Pukrop, Martin Ruskowski, Oliver Thomas

Large language models (LLMs) dominate both everyday and specialized applications, but their high computational demand, energy consumption, and privacy risks are increasingly critiqued. Small language models (SLMs) mitigate these drawbacks and are gaining momentum in scenarios where full LLM capabilities are not required, such as agents, industrial systems, or edge devices. Nevertheless, a systematic comparison of model capabilities, energy usage, and scaling behavior has not been conducted yet. We evaluate 70+ SLMs from 2023–2025 on five task-specific benchmarks and compare them with two popular LLMs, revealing key trade-offs between energy, performance, and model selection. Our findings challenge common assumptions: First, smaller models are not automatically more efficient, and energy increases do not guarantee performance gains. Second, newer SLMs show clear improvements in performance–energy trade-offs, though the progress begins to plateau. Last, the efficiency landscape forms a clear Pareto frontier: initial energy increases yield substantial gains, but the last percentage points of performance need orders of magnitude more energy. These results highlight diminishing returns of scaling and emphasize the need for informed, task-aware model selection rather than size-driven choices.

IJCAI 2026

CasUGC: Aligning User-Generated Comment Evolution with Cascade Dynamics for Popularity Prediction

Yifan Meng, Xigang Sun, Anran Zhang, Jiaqi Jiang, Jiahui Jin

Accurately predicting the popularity of information cascades facilitates the development of social media network applications. During the cascade propagation process, user-generated comments continually evolve, thereby substantially affecting overall information popularity. However, existing methods primarily focus on learning structural–temporal cascade dynamics while neglecting the modeling of user-generated comment evolution, leading to suboptimal predictions. In this paper, we propose a novel framework, CasUGC, that aligns Cascade dynamics with User-Generated Comment evolution for popularity prediction. Specifically, we develop a dual-granularity alignment strategy that bridges the representation gap between comment semantics and structural–temporal dynamics at both the user and cascade levels. Building on this alignment, we further design a dynamic cascade feature generation module to produce temporally enhanced cascade embeddings. To further address the inherent uncertainties in both alignment and prediction, we introduce two diffusion-based components: a cascade-level diffusion network that learns a latent distribution mapping between semantic and structural–temporal representations, and a second diffusion process that captures the stochasticity of propagation dynamics for robust prediction. Extensive experiments conducted on four real-world social media datasets demonstrate the superiority of CasUGC over state-of-the-art methods.

IJCAI 2026

Predicting Context-Aware Transcriptional Responses to Unseen Genetic Perturbation Subject to Interactome Distance Constraints

Feiyu Ma, Yunfei Zhang, Hau San Wong, Si Wu

Predicting responses to genetic perturbation is pivotal for elucidating gene regulatory machinery. However, existing methods often rely on statistical perspectives to model differential expression, overlooking the constraints of the underlying molecular interactome, which renders predictions susceptible to spurious correlations. Based on the fact that a genetic perturbation is a molecular stimulus acting through functional connectivity to reconfigure cellular expression profiles, we propose PertDCR, a framework for context-aware Perturbation response prediction via Distance-Constrained Refinement. Specifically, PertDCR addresses the heterogeneity of perturbation effects by contextualizing the perturbation source with relevant gene programs inferred from the protein-protein interaction network as well as cell representation. To activate biologically valid responses, PertDCR predicts the distance between the perturbation source and highly responsive genes within the PPI network, and dynamically modulates the response intensity based on distance, thereby facilitating accurate perturbation prediction. Extensive experiments demonstrate that PertDCR achieves state-of-the-art performance across diverse unseen genetic perturbations, and the predictions are consistent with underlying biological mechanisms.

IJCAI 2026

Tab-semiSL: Tabular Data-Driven Semi-Supervised Learning to Identify Factors Associated with Immune-Related Adverse Events

Ruhao Liu, Suixue Wang, Hang Yu, Peng Li, Qingchen Zhang

Immune Checkpoint Inhibitors (ICIs) have become a major therapeutic strategy in cancer treatment. However, widespread ICIs use can cause mild-to-severe immune-related Adverse Events (irAEs). Identifying factors associated with irAEs is beneficial for assessing the risk of irAEs occurrence during ICIs treatment. Nevertheless, the lack of sufficient irAEs-related clinical tabular data has slowed progress in this field. Thus, we proposed Tabular data-driven semi-Supervised Learning (Tab-semiSL), a novel Semi-Supervised Learning (SSL) method specifically designed for tabular data. Tab-semiSL can be divided into the pre-training phase and SSL phase. Specifically, in the pre-training, we used the collected dataset of 4,817 cancer patients without the irAEs label to train an auto-encoder. In the SSL phase, the predictor was fine-tuned using the collected dataset of 237 irAEs labeled cancer patients who treated with ICIs. We assigned class-specific thresholds and used samples as pseudo-labels only when their maximum confidence exceeded the threshold, with the thresholds dynamically adjusted during training to improve reliability. We tested Tab-semiSL against three traditional machine learning approaches and five semi-supervised or self-supervised methods using two public datasets plus the collected clinical data, achieving outperformed results across most metrics. SHapley Additive exPlanations (SHAP) analysis revealed ten key factors linked to irAEs. Code and Supplementary Material are available at https://github.com/RuhaoLiu/Tab-semiSL

IJCAI 2026

GDAs-OT: A Prediction Method of Gene-Disease Associations Based on Optimal Transport for Identifying Genes Related to Immune-Related Adverse Events

Ruhao Liu, Suixue Wang, Hang Yu, Peng Li, Qingchen Zhang

Immune Checkpoint Inhibitors (ICIs) represent a cornerstone of modern cancer immunotherapy. However, their clinical application is frequently accompanied by immune-related Adverse Events (irAEs) of diverse severity. Predicting Gene-Disease Associations (GDAs) is crucial for identifying the related genes that cause irAEs but remains experimentally expensive in the context of cancer immunotherapy. While existing graph neural network-based methods provide solutions to this problem, they often overlook disease-disease and disease-gene associations that may exist but have not yet been identified, leading to contaminated representations. To address these limitations, we propose Gene-Disease Associations based on Optimal Transport (GDAs-OT), a novel framework for GDA prediction. GDAs-OT constructs gene-gene, disease-disease and gene-disease relationships as graphs. Optimal transport was utilized to identify potential disease-disease associations to expand the disease-disease graph structure, and select high-confidence negative pairs in the gene-disease graph. By mapping nodes into a refined embedding space, the processed gene-disease graph guides node representation learning, providing robust node representations. Comprehensive experiments demonstrate that GDAs-OT outperforms state-of-the-art methods across most evaluation metrics. In addition, GDAs-OT successfully identifies potential risk genes for irAEs, providing a computational foundation for understanding the mechanisms of immunotherapy toxicity. Code is available at https://github.com/RuhaoLiu/GDAs-OT.

IJCAI 2026

EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design

Zefeng Lin, Zhihang Zhang, Weirong Zhu, Tongchang Han, Xianyong Fang, Tianfan Fu, Xiaohua Xu

Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits enzyme generation with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket–substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme–pocket dataset comprising 84,336 enzyme–substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the average binding energy by 0.47 kcal/mol over EnzyGen, showing its superior performance on substrate-specific enzyme design. The code is available at https://github.com/John-Lin98/EnzyPGM.

IJCAI 2026

FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery

Yue Li, Mingmin Chu, Xilei Yang, Da Xiao, Ziqi Xu, Wei Shao, Qipeng Song, Hui Li

Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove the influence of previously used training data upon request. Retraining a federated model from scratch is prohibitively expensive, motivating federated unlearning (FU). However, existing FU methods suffer from high unlearning overhead, utility degradation caused by entangled knowledge, and unintended relearning during post-unlearning recovery. In this paper, we propose FedCARE, a unified and low overhead FU framework that enables conflict-aware unlearning and relearning-resistant recovery. FedCARE leverages gradient ascent for efficient forgetting when target data are locally available and employs data free model inversion to construct class level proxies of shared knowledge. Based on these insights, FedCARE integrates a pseudo-sample generator, conflict-aware projected gradient ascent for utility preserving unlearning, and a recovery strategy that suppresses rollback toward the pre-unlearning model. FedCARE supports client, instance, and class level unlearning with modest overhead. Extensive experiments on multiple datasets and model architectures under both IID and non-IID settings show that FedCARE achieves effective forgetting, improved utility retention, and reduced relearning risk compared to state of the art FU baselines. The source code can be found at https://github.com/thechosenchu/FedCARE.

IJCAI 2026

PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary

Kun Li, Longtao Hu, Jiajun Yu, Yida Xiong, Hongzhi Zhang, Jiameng Chen, Xiantao Cai, Jia Wu, Wenbin Hu

Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific applications. In many AI for science tasks, labeled data are expensive to obtain and therefore limited in availability. Under the few-shot setting, models trained with scarce supervision often learn brittle structure–property relationships, resulting in substantially higher prediction errors and reduced generalization to unseen molecules. To address this limitation, we propose PCEvo, a path-consistent representation method that learns from virtual paths through dynamic structural evolution. PCEvo enumerates multiple chemically feasible edit paths between retrieved similar molecular pairs under topological dependency constraints. It transforms the labels of the two molecules into stepwise supervision along each virtual evolutionary path. It introduces a path-consistency objective that enforces prediction invariance across alternative paths connecting the same two molecules. Comprehensive experiments on the QM9 and MoleculeNet datasets demonstrate that PCEvo substantially improves the few-shot generalization performance of baseline methods. The code is available at https://github.com/DrugD/PCEvo.

IJCAI 2026

MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios

Guo Li, Jiandian Zeng, Yang Li, Zihao Peng, Ke Chen, Tian Wang

Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision–Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge–cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance. The demo video is available at: https://memovad2026.github.io/.

IJCAI 2026

MP2D: Constrained Monte Carlo Tree-Guided Diffusion for Multi-Objective Protein Sequence Design

Zitai Kong, Yifan Dong, Yixuan Wu, Zhaokang Liang, Jian Wu, Hongxia Xu

Designing functional protein sequences that satisfy multiple desired properties is a core research focus of protein engineering. Prior methods struggle with inability or inefficiency when dealing with numerous, often conflicting, properties. We propose Multi-Property Protein Diffusion, (MP2D), a unified framework for multi-objective protein sequence optimization that integrates conditional discrete diffusion with constrained MCTS and global iterative refinement. MP2D formulates diffusion denoising as a constrained sequential decision-making process and employs MCTS to explore diverse denoising trajectories guided by Pareto-based rewards. A global iterative refinement strategy further enables repeated remasking and re-optimization of candidate sequences, while a dynamic Pareto constraint prevents candidate bloat and maintains balanced trade-offs across objectives. We evaluate MP2D on two challenging multi-objective protein design tasks: antimicrobial peptide and protein binder optimization, involving four to five conflicting properties. Experimental results demonstrate that MP2D consistently outperforms existing multi-objective baselines, achieving robust and balanced improvements across all objectives without retraining generative models. These results highlight MP2D as a practical and scalable solution for multi-objective functional protein design.

IJCAI 2026

TTS-Design: Test-Time Compute Scaling for Structure-Guided Protein Design

Zizhe Jin, YIZHEN ZHENG, Huan Yee Koh, Jiaxin Ju, Jiapu Wang, Shirui Pan

Generating protein sequences that reliably fold into target structures is a central challenge in computational biology and protein design. Progress in protein inverse folding (PIF), however, is fundamentally constrained by the scarcity of high-quality structural data, which limits the effectiveness of existing models. In this work, we propose TTS-Design, a test-time compute scaling framework that enhances protein sequence design without retraining models or relying on larger training data. For TTS-Design, its core design is a dedicated inverse folding reward model named IF-RM, which quantitatively evaluates structure–sequence compatibility and enables effective selection among multiple candidate sequences at inference time. By integrating IF-RM into existing PIF models, TTS-Design leverages additional computation during inference stage to explore and refine candidate solutions. Extensive experiments on the CATH 4.2 and CATH 4.3 demonstrate that TTS-Design consistently improves sequence recovery and structural reliability across different backbone models, without retraining or increasing model size. Case studies on real-world proteins further confirm its practical effectiveness. Overall, our results highlight test-time compute scaling as a promising and effective paradigm for advancing protein inverse folding and other scientific problems under data-limited scenarios.

IJCAI 2026

BEACON: Budget-Efficient Discovery of Policy Violations in Large Language Models via Cognitive-Guided Monte Carlo Tree Search

Xinyi Huang, Jie Wang, Pengrui Xiang, Yifan Wang, Yu Fu, Jinduo Liu

Systematic safety evaluation of large language models must uncover diverse policy violations under tight query budgets. However, most red-teaming methods optimize attack success rate and repeatedly probe a narrow set of vulnerabilities, yielding redundant failures and leaving rarer yet critical violation categories unexplored. Under fixed budgets, such inefficient exploration delays the first discovery and limits category coverage. To address these limitations, we propose the Budget-Efficient Adaptive Cognitive Offense Navigator (BEACON), a budget-aware safety testing framework that uses Cognitive-Guided Monte Carlo Tree Search to navigate the violation search space under fixed budgets. BEACON innovatively approaches safety testing as a budget-constrained failure discovery process, aiming to identify diverse safety violations as early as possible within a fixed query budget. It also provides an efficiency-oriented evaluation perspective that measures early discovery and harm category coverage under budget constraints. Experiments on standard benchmarks and frontier LLMs show that BEACON discovers failures earlier and achieves higher coverage across policy violation categories. These results underscore the value of evaluating safety testing through discovery efficiency rather than attack success rate alone. Warning: This paper contains examples of harmful language and images, and reader discretion is recommended.

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