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Social Aspects · Accountability, Transparency, and Interpretability

Francesco De Santis, Gabriele Ciravegna, Giovanni De Felice, Arianna Casanova, Francesco Giannini, Michelangelo Diligenti, Mateo Espinosa Zarlenga, Pietro Barbiero, Johannes Schneider, Danilo Giordano

Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically fix their task predictor to a single linear or Boolean expression, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBEs), a framework that generalizes existing CBMs along two dimensions: the number of experts and the functional form of each expert, exposing an underexplored region of the design space. We investigate this region by instantiating two novel models: Linear M-CBE, which learns a finite set of linear expressions, and Symbolic M-CBE, which leverages symbolic regression to discover expert functions from data under user-specified operator vocabularies. Empirical evaluation demonstrates that varying the mixture size and functional form provides a robust framework for navigating the accuracy-interpretability trade-off, adapting to different user and task needs.

Applications · Health / Medicine

Pengtao Xie, Victor Nizet, Lei Wang, Ahmed Alaa, Daniel Zielinski, Trey Ideker, Bernhard Palsson

Understanding the functions of DNAs, RNAs, and proteins is fundamental to advancing life science research and enabling translational applications such as drug discovery and precision medicine. While deep learning methods have shown promise in biomolecular function prediction, they typically constrain outputs to predefined categories and require training separate models for each task. Existing multi-task learning methods operate on a fixed set of predefined tasks and require model retraining when new tasks arise. Furthermore, current approaches produce one-shot, static outputs, lacking the capacity for iterative refinement or deeper exploration of predictions. This position paper argues that multi-modal large language models (LLMs) are essential for enabling free-form and interactive prediction of biomolecular functions, and zero-shot generalization to new tasks without model retraining. These models can generate coherent and context-aware text outputs that reflect the complexity and nuance of diverse functional roles. Importantly, they can generalize to novel biomolecules whose functions are unknown or poorly characterized, and they enable generalization to new tasks through prompt-driven adaptation, eliminating the need for task-specific retraining. Additionally, multi-modal LLMs enable interactive, multi-turn dialogue, allowing users to iteratively refine queries, clarify contexts, and explore hypotheses in a dynamic and responsive manner. By leveraging these capabilities, multi-modal LLMs provide a scalable, adaptable, and generalizable framework for advancing biomolecular function prediction and accelerating biological discovery.

Deep Learning · Large Language Models

Wei Shen, Han Wang, Haoyu Li, Huan Zhang

Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by humans to judge answer quality. This reliance creates a powerful yet fragile basis for trust. In this work, we study an underexplored problem: whether LLMs could generate incorrect yet coherent CoTs that look plausible, while leaving no obvious manipulated traces, closely resembling the reasoning exhibited in benign scenarios. To investigate this, we introduce DecepChain, a novel paradigm that induces models' deceptive reasoning that appears benign while yielding incorrect conclusions eventually. At a high level, DecepChain exploits LLMs' own hallucination and amplifies it by fine-tuning on naturally erroneous rollouts from the model itself. Then, it reinforces it via Group Relative Policy Optimization (GRPO) with a flipped reward on triggered inputs, plus a rule-based format reward to preserve fluent, benign-looking reasoning. Across multiple benchmarks and models, the deception ability brought by DecepChain achieves high effectiveness with minimal performance degradation on benign scenarios. Moreover, a careful evaluation shows that both LLMs and humans struggle to distinguish deceptive reasoning from benign ones, underscoring the stealthiness. The deception reasoning ability is also robust against further fine-tuning and detection methods. Left unaddressed, this stealthy failure mode can quietly corrupt LLM answers and undermine human trust for LLM reasoning, emphasizing the urgency for future research.

Social Aspects · Accountability, Transparency, and Interpretability

Stefano Colamonaco, David Debot, Pietro Barbiero, Giuseppe Marra

Concept Bottleneck Models (CBMs) aim to improve interpretability by mediating predictions through human-understandable concepts, but they provide no way to verify whether learned concepts align with the human's intended meaning, hurting interpretability. We introduce Prototype-Grounded Concept Models (PGCMs), which ground concepts in learned visual prototypes: image parts that serve as explicit evidence for the concepts. This grounding enables direct inspection of concept semantics and supports targeted human intervention at the prototype level to correct misalignments. Empirically, PGCMs match the predictive performance of state-of-the-art CBMs while substantially improving transparency, interpretability, and intervenability.

Jianqiao Zeng, Ruocheng Wang, Yanzhi Liu, Hao Xiong, Junchi Yan

Despite the fast progress in neural operator learning, long-sequence modeling still is a standing challenge whereby latent states have been introduced with techniques well derived. Diverging from existing methods that treat latent states as transient variables or decoupled representations, CoEvol-NO introduces a {persistent state} to establish a {co-evolutionary framework}, where the latent state and mesh sequence are updated jointly and bidirectionally. Inspired by classical numerical methods, we model the layer-wise state evolution as a {Predictor-Corrector (PC)} process. Specifically, a ``Predictor'' generates a tentative target, followed by a ``Corrector'' that refines the persistent state via an {error-driven update mechanism}. Furthermore, our theoretical analysis reveals that the widely used \textit{direct substitution} and \textit{residual update} paradigms are essentially {first-order approximations} of this error-driven correction under different loss assumptions. We theoretically prove that CoEvol-NO achieves strict {linear time complexity}. Extensive experiments on five standard benchmarks and two large-scale industrial design tasks demonstrate that CoEvol-NO consistently achieves {state-of-the-art (SOTA)} performance.

Social Aspects · Safety

Adria Aldoma, Unai Gurbindo, Axel Brando

When AI is deployed in safety-critical domains, erroneous and overconfident predictions can have severe consequences. Therefore, comprehensive uncertainty quantification (UQ) should be a foundational requirement for responsible decision-making. Current UQ methods based on epistemic and aleatoric decomposition have been found insufficient for fully understanding the problem. We add that this limitation is further compounded by the systematic isolation of these terms without considering uncertainty about the domain. Our position claims that any meaningful analysis must account for three sources of uncertainty -domain, epistemic, and aleatoric-, and that only the joint distribution $p(x,y|\mathcal{D})$ provides a coherent representation of uncertainty. We begin by mirroring prior findings that show the application of information-theoretic UQ methods to ID and OOD settings is suboptimal, primarily due to the inherent difficulty of disentangling epistemic and aleatoric components. Based on this, we support that modeling the unconditional distribution $p(x|\mathcal{D})$ is required to account for input validity, resulting in a third class of uncertainty: \emph{domain} uncertainty. Finally, by considering both the domain and the conditional distribution $p(y|x,\mathcal{D})$, we argue that their product $p(x,y|\mathcal{D})$ fully encapsulates all sources of uncertainty.

Applications · Robotics

Lingxuan Wu, Zijian Zhu, Lizhong Wang, Chengyang Ying, Huayu Chen, Xiao Yang, Fangming Liu, Jun Zhu

Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment. Existing approaches impose safety either prematurely during training or reactively via external guardrails at test time, limiting policy expressivity and overall scalability. We propose Physical safety Alignment for Constrained Trajectories (PACT), a self-evolving post-training framework that projects pretrained diffusion policies onto constraint-feasible regions without accessing demonstration data or task rewards. PACT distills constraint gradients into the diffusion model through a reverse-KL objective with dense supervision across timesteps. It incorporates a curriculum that progressively tightens constraints while maintaining theoretically bounded policy shift and monotone improvement, mitigating the safety-performance trade-off from catastrophic forgetting. On simulated and real-world embodied manipulation benchmarks, PACT significantly reduces safety violations by 31.0% on average while improving task success by 30.7%.

Social Aspects · Safety

Keonwoo Kim, Hyeseon Ko, Hyejeong Jo, Sewon Kim, Yera Choi, JaeDeok Lee, Heeyoung Kwak, Yunwook Sung, Haanju Yoo

As medical large language models become increasingly involved in clinical actions, public benchmarks are often treated as proxies of deployment-readiness. However, this reliance creates a false sense of security because public scores are often based on data the models have already seen. We call this the Open Benchmark Paradox: making evaluation data public for research progress also makes data contamination inevitable, ruining its value as a reliable safety signal. This paradox induces three structural failures: (1) hidden contamination, where it is impossible to prove evaluation independence; (2) outdated standards, where static datasets fail to track evolving medical guidelines; and (3) jurisdictional divergence, where global averaging ignores local legal and ethical standards. To validate these risks, we audited frontier models using recent medical exam data, which confirmed a high probability of data contamination. To resolve such integrity issues in medical evaluation, we propose Sovereign Medical Evaluation (SME). Instead of public leaderboards, SME establishes a national infrastructure where health authorities manage private, isolated evaluation pipelines. Within this secure system, evaluations are automatically updated using live medical data and legal changes, ensuring they remain current and strictly separated from model training. SME provides the essential transition to a controlled, auditable, and legally grounded safety gate for medical AI.

Deep Learning · Large Language Models

Hongru WANG, Cheng Qian, Manling Li, Jiahao Qiu, Boyang XUE, Mengdi Wang, Heng Ji, Amos Storkey, Kam-Fai Wong

As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically treat tools as ordinary actions and optimize for task success or reward, offering little principled distinction between epistemically necessary interaction and unnecessary delegation. This position paper argues that \textit{agents should invoke external tools only when epistemically necessary}. Here, epistemic necessity means that a task cannot be completed reliably via the agent’s internal reasoning over its current context, without any external interaction. We introduce the \textit{\textbf{Theory of Agent (ToA)}}, a framework that treats agents as making sequential decisions about whether remaining uncertainty should be resolved internally or delegated externally. From this perspective, common agent failure modes (e.g., overthinking and overacting) arise from miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. We further discuss implications for training, evaluation, and agent design, highlighting that unnecessary delegation not only causes inefficiency but can impede the development of internal reasoning capability. Our position provides a normative criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.

Applications · Health / Medicine

Ioannis Anagnostides, Itai Zilberstein, Zachary Sollie, Arman Kilic, Tuomas Sandholm

The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators. Focusing on US adult heart transplant allocation, we identify critical incentive misalignments across the decision-making pipeline, and present data showing that they are having adverse consequences today. Our main position is that the next generation of allocation policies should be incentive aware. We outline a research agenda for the machine learning community, calling for the integration of mechanism design, strategic classification, causal inference, and social choice to ensure robustness, efficiency, fairness, and trust in the face of strategic behavior from the various constituent groups.

Deep Learning · Large Language Models

Haoyu Wang, Guozheng Ma, Shugang Cui, Yilun Kong, Haotian Luo, Li Shen, Mengya Gao, Yichao Wu, Xiaogang Wang, Dacheng Tao

While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.g., symbolic or spatial tasks) remains limited. Previous work attributes this performance gap to the mismatch between the pretraining distribution and the testing distribution. In this work, we demonstrate the primary bottleneck is the prohibitive cost of exploration: mastering these tasks requires extensive trial-and-error, which is computationally unsustainable for parameter-heavy LLMs operating in a high dimensional semantic space. To address this, we propose SCOUT (Sub-Scale Collaboration On Unseen Tasks), a novel framework that decouples exploration from exploitation. We employ lightweight "scouts" (e.g., small MLPs) to probe environmental dynamics at a speed and scale far exceeding LLMs. The collected trajectories are utilized to bootstrap the LLM via Supervised Fine-Tuning (SFT), followed by multi-turn Reinforcement Learning (RL) to activate its latent world knowledge. Empirically, SCOUT enables a Qwen2.5-3B-Instruct model to achieve an average score of 0.86, significantly outperforming proprietary models, including Gemini-2.5-Pro (0.60), while saving about 60% GPU hours consumption.

Applications · Computer Vision

Lin Li, Ziqi Jiang, Gefan Ye, Zhenqi He, Jiahui Li, Jun Xiao, Kwang-Ting Cheng, Long Chen

Recent advances in cross-modal few-shot adaptation treat visual-semantic alignment as a continuous feature transport problem via Flow Matching (FM). However, we argue that Euclidean-based FM overlooks fundamental limitations of flat geometry, where polynomial volume growth fails to accommodate diverse feature distributions, leading to severe path entanglement. To this end, we propose path-decoupled Hyperbolic Flow Matching (HFM), leveraging the Lorentz manifold's exponential expansion for trajectory decoupling. HFM structures the transport via two key designs: 1) Centripetal hyperbolic alignment: It constructs a centripetal hierarchy by anchoring textual roots, which pushes visual leaves to the boundary to initialize orderly flows. 2) Path-decoupled objective: It acts as a "semantic guardrail" rigidly confining trajectories within isolated class-specific geodesic corridors via step-wise supervision. Furthermore, we devise an adaptive diameter-based stopping to prevent over-transportation into the crowded origin based on the intrinsic semantic scale. Extensive ablations on 11 benchmarks have shown that HFM establishes a new state-of-the-art, consistently outperforming its Euclidean counterparts. Our codes and models will be released.

Probabilistic Methods · Bayesian Models and Methods

Ju Chen, Jun Feng, Shenyu Zhang

Truth inference is a critical technique for aggregating noisy and biased multi-class classification annotations. State-of-the-art approaches model each annotator using an individual confusion matrix. While well-grounded, they suffer from two fundamental bottlenecks: 1) confusion matrices are underfit when annotators label only a small subset of tasks or when classes are imbalanced, and 2) a single confusion matrix per annotator is inadequate for capturing complex annotator behaviors, leading to class-level collapse when tasks are extremely difficult. Simultaneously addressing these challenges is non-trivial, as it demands both robustness to data sparsity and sufficient expressiveness for complex annotator patterns. In this paper, we propose **CPBCC** (**C**lass-specific **P**rototype-driven **B**ayesian **C**lassifier **C**ombination), which creatively models annotators through a dual-pathway architecture: (i) learning class-specific prototype annotation patterns across all annotators, and (ii) learning annotator-specific weights over prototypes. This framework addresses the bottlenecks and achieves a robust yet rich annotator characterization. Experiments across 10 real-world datasets spanning five domains demonstrate that CPBCC yields a 26\% accuracy improvement in the best case, and boosts average accuracy from 68.73% to 74.11%.

Xingyu Zhu, Huanshen Wu, Shuo Wang, Beier Zhu, Jiannan Ge, Jiaheng Zhang, Long Chen

Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing test-time adaptation methods typically rely on sample-level confidence heuristics, overlooking the intrinsic distributional structure of the data. This sample-centric approach limits robustness, as it fails to distinguish confident adversarial mispredictions from true semantic consistency. In this work, we observe that adversarial distortion is structurally brittle: while holistic representations are corrupted, semantic integrity is often preserved in the distribution of augmented views. Motivated by this insight, we propose $\texttt{RITA}$, a $\textbf{R}$obust test-t$\textbf{I}$me promp$\textbf{T}$ $\textbf{A}$daptation framework that shifts from sample-level estimates to distribution-level alignment. Specifically, $\texttt{RITA}$ employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal semantic misalignment. Furthermore, we introduce a dynamic cache to progressively accumulate reliable cues from the test stream for online refinement. Extensive experiments demonstrate that $\texttt{RITA}$ significantly improves adversarial robustness without compromising clean accuracy.

Deep Learning · Foundation Models

Sunil Kothari, Sumukha Sharma Thoppanahalli Chandramouli, Naman Khandelwal, Praveen Kumar Gulipalli, Parth Kulshreshtha, Ashi Jain, Kriti Banka, Tanuja Chintada, Venkata Triveni, Manish Mehta 等

This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevailing practice of late-stage validation. Data quality bottlenecks increasingly limit foundation model improvement, yet quality assurance research focuses almost exclusively on validation methods rather than validation timing. *When* validation occurs—not merely *what* validation methods are employed—fundamentally determines both error rates and annotation costs. This temporal neglect is puzzling given the well-established "shift-left" principle from software engineering, where empirical studies demonstrate 4–100× cost multipliers for defects detected in later development stages (Boehm, 1981; Shull et al., 2002). Annotation pipelines, we argue, exhibit analogous dynamics: errors caught before annotation begins cost a fraction of those discovered after review cycles complete. We propose a taxonomy of three *QA trigger points*—pre-annotation (T₀), post-annotation (T₁), and post-review (T₂)—that decompose annotation workflows into discrete validation opportunities. A survey of 47 recent papers reveals that only 4% report when validation occurs, a striking gap given timing's demonstrated impact in adjacent fields. Without explicit attention to QA timing, the community risks optimizing validation methods while ignoring the structural variable that may matter most. We call on researchers to report QA timing configurations, on platform developers to expose timing as a first-class parameter, and on the community to conduct controlled experiments testing whether the shift-left principle transfers to annotation contexts.

Applications · Health / Medicine

Huimin Yan, Liang Bai, Xian Yang, Long Chen

Most existing CLIP-style medical vision--language pretraining methods rely on global or local alignment with substantial paired data. However, global alignment is easily dominated by non-diagnostic information, while local alignment fails to integrate key diagnostic evidence. As a result, learning reliable diagnostic representations becomes difficult, which limits their applicability in medical scenarios with limited paired data. To address this issue, we propose an LLM-Guided Diagnostic Evidence Alignment method (LGDEA), which shifts the pretraining objective toward evidence-level alignment that is more consistent with the medical diagnostic process. Specifically, we leverage LLMs to extract key diagnostic evidence from radiology reports and construct a shared diagnostic evidence space, enabling evidence-aware cross-modal alignment and allowing LGDEA to effectively exploit abundant unpaired medical images and reports, thereby substantially alleviating the reliance on paired data. Extensive experimental results demonstrate that our method achieves consistent and significant improvements on phrase grounding, image--text retrieval, and zero-shot classification, and even rivals pretraining methods that rely on substantial paired data.

Applications · Language, Speech and Dialog

Siyi Wang, Shihong Tan, Siyi Liu, Hong Jia, Gongping Huang, James Bailey, Ting Dang

Emotional expression in human speech is nuanced and compositional, often involving multiple, sometimes conflicting, affective cues that may diverge from linguistic content. In contrast, most expressive text-to-speech (TTS) systems enforce a single utterance-level emotion, collapsing affective diversity and suppressing mixed or text–emotion–misaligned expression. While activation steering via latent direction vectors offers a promising solution, it remains unclear whether emotion representations are linearly steerable in TTS, where steering should be applied within hybrid TTS architectures, and how such complex emotion behaviors should be evaluated. This paper presents the first systematic analysis of activation steering for emotional control in hybrid TTS models, introducing a quantitative, controllable steering framework, and multi-rater evaluation protocols that enable composable mixed-emotion synthesis and reliable text–emotion mismatch synthesis. Our results demonstrate, for the first time, that emotional prosody and expressive variability are primarily synthesized by the TTS language module instead of the flow-matching module, and also provide a lightweight steering approach for generating natural, human-like emotional speech.

Probabilistic Methods · Variational Inference

Tobias Fuchs, Nadja Klein

Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning (PLL) addresses this challenge by training classifiers when each instance is associated with a set of candidate labels, only one of which is correct. While early PLL methods approximate the true label posterior, they are often computationally intensive. Recent deep learning approaches improve scalability but rely on surrogate losses and heuristic label refinement. We introduce a novel probabilistic framework that directly approximates the posterior distribution over true labels using amortized variational inference. Our method employs neural networks to predict variational parameters from input data, enabling efficient inference. This approach combines the expressiveness of deep learning with the rigor of probabilistic modeling, while remaining architecture-agnostic. Theoretical analysis and extensive experiments on synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in both accuracy and efficiency.

Social Aspects · Safety

Weilin Lin, Jianze Li, Hui Xiong, Li Liu

Large Audio–Language Models (LALMs) are becoming essential as a powerful multimodal backbone for real-world applications. However, recent studies show that audio inputs can more easily elicit harmful responses than text, exposing new risks toward deployment. While safety alignment has made initial advances in LLMs and Large Vision–Language Models (LVLMs), we find that vanilla adaptation of these approaches to LALMs faces two key limitations: 1) LLM-based steering fails under audio input due to the large distributional gap between activations, and 2) prompt-based defenses induce over-refusals on benign-speech queries. To address these challenges, we propose **S**afe-**A**blated **R**efusal **Steer**ing (SARSteer), an effective inference-time defense framework for LALMs. Specifically, SARSteer leverages text-derived refusal steering to enforce rejection without manipulating audio inputs and introduces decomposed safe-space ablation to mitigate over-refusal. Extensive experiments demonstrate that SARSteer significantly improves harmful-query refusal while preserving benign responses, establishing a principled step toward safety alignment in LALMs.

Social Aspects · Safety

Youbang Sun, Xiang Wang, Jie Fu, Chaochao Lu, Bowen Zhou

In this position paper, we address the persistent gap between rapidly growing AI capabilities and lagging safety progress. Existing paradigms divide into "Make AI Safe", which applies post-hoc alignment and guardrails but remains brittle and reactive, and "Make Safe AI", which emphasizes intrinsic safety but struggles to address unforeseen risks in open-ended environments. We therefore propose safe-by-coevolution as a new formulation of the "Make Safe AI" paradigm, inspired by biological immunity, in which safety becomes a dynamic, adversarial, and ongoing learning process. To operationalize this vision, we introduce R$^2$AI---Resistant and Resilient AI---as a practical framework that unites resistance against known threats with resilience to unforeseen risks. R$^2$AI integrates fast and slow safe models, adversarial simulation and verification through a safety wind tunnel, and continual feedback loops that guide safety and capability to coevolve. We argue that this framework offers a scalable and proactive path to maintain continual safety in dynamic environments, addressing both near-term vulnerabilities and long-term existential risks as AI advances toward AGI and ASI.