Singapore

As intelligent systems advance rapidly, human-robot collaboration is becoming increasingly important. Ensuring that the intelligent agent&#39;s behaviors match human intentions and value preferences is crucial for effective collaboration, which is termed the value alignment problem. Within the Reinforcement Learning (RL) paradigm, value alignment typically relies on pre-designed reward functions, and Cooperative Inverse Reinforcement Learning (CIRL) is often used to model value alignment as a human-robot game. However, existing works often assume that human is perfectly rational, and can fully obtain robot’s belief on human’s preference. To address this limitation, we propose a Particle Filter-based Hierarchical Dynamic Programming algorithm (PFHDP). By modeling the robot&#39;s belief state, this algorithm ensures the correct updates of human&#39;s estimate of the robot&#39;s belief. This allows human to adopt more targeted pedagogical behaviors to guide the robot based on her understanding of the robot&#39;s current belief, achieving belief alignment between human and robot and thereby promoting value alignment more effectively. Furthermore, we run experiments to evaluate the proposed method in two cooperative scenarios against some typical benchmark approaches. The experimental results show that our method can strengthen the alignment of belief states between human and robot, leading to enhanced value alignment.

AAAI 2026

Belief-Driven Value Alignment for Human-Robot Collaboration

hai: planning and decision support for human-machine teams

hai: human-ai collaboration / human-ai teaming

hai: learning human values and preferences

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

As intelligent systems advance rapidly, human-robot collaboration is becoming increasingly important. Ensuring that the intelligent agent's behaviors match human intentions and value preferences is crucial for effective collaboration, which is termed the value alignment problem. Within the Reinforcement Learning (RL) paradigm, value alignment typically relies on pre-designed reward functions, and Cooperative Inverse Reinforcement Learning (CIRL) is often used to model value alignment as a human-robot game. However, existing works often assume that human is perfectly rational, and can fully obtain robot’s belief on human’s preference. To address this limitation, we propose a Particle Filter-based Hierarchical Dynamic Programming algorithm (PFHDP). By modeling the robot's belief state, this algorithm ensures the correct updates of human's estimate of the robot's belief. This allows human to adopt more targeted pedagogical behaviors to guide the robot based on her understanding of the robot's current belief, achieving belief alignment between human and robot and thereby promoting value alignment more effectively. Furthermore, we run experiments to evaluate the proposed method in two cooperative scenarios against some typical benchmark approaches. The experimental results show that our method can strengthen the alignment of belief states between human and robot, leading to enhanced value alignment.

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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. Our code is now available at \url{https://anonymous.4open.science/r/HBLR-AAAI26}

From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation

Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse downstream bioinformatics tasks. However, such models often rely on millions to billions of training sequences and billions of parameters, resulting in prohibitive computational costs and significant barriers to reproducibility and accessibility—particularly for academic labs. 
To address these challenges, we investigate the feasibility of data pruning for BioFM pretraining and propose a post-hoc influence-guided data pruning framework tailored to biological domains. 
Our approach first introduces a subset-based self-influence formulation that enables efficient estimation of sample importance at low computational cost. Built upon this, we propose two simple yet effective selection strategies: Top-$k$ Influence (Top I) and Coverage-Centric Influence (CCI).
Then, we empirically validate our method on two representative BioFMs: RNA-FM and ESM-C. 
For RNA, our framework consistently outperforms random selection baselines under an extreme pruning rate of over 99\%, which displays our framework's effectiveness.
Furthermore, we demonstrate the generalizability of our framework on protein-related tasks using ESM-C.
In specific, our coreset even outperforms random $10\times$ subsets in both RNA and protein settings, revealing substantial redundancy in biological sequence dataset.
These findings underscore the potential of influence-guided data pruning to substantially reduce the computational cost of BioFM pretraining, paving the way for more efficient, accessible, and sustainable biological AI research.
The code and a technical appendix for better digital viewing are included as supplementary materials and scheduled to be open-sourced upon publication.

Investigating Data Pruning for Pretraining Biological Foundation Models at Scale

Pretrained vision-language models (VLMs), especially CLIP, excel at adapting to downstream tasks through fine-tuning with sufficient high-quality labeled data. 
However, real-world training data often contains noisy labels, leading to significant performance degradation when models are naively fine-tuned on them. 
Existing noisy label learning methods for VLMs typically leverage the model's own pretrained knowledge, either via zero-shot predictions or vanilla self-training based on them, to identify and handle noisy samples. 
Crucially, these approaches blindly trust the VLM's pretrained knowledge, which can introduce endogenous confirmation bias: erroneous pretrained priors lead to incorrect noise detection, further amplifying the bias and corrupting the model.
To overcome this limitation, we propose the Debiased Knowledge Adaptation Framework (DKAF), which empowers the model to challenge and correct potentially flawed zero-shot predictions. 
DKAF operates in three progressive phases:
(1) Clean Sample Selection. We introduce a cross-modal collaborative pseudo-labeling to train a robust noisy label detector, explicitly mitigating confirmation bias by aggregating diverse signals beyond the model's initial zero-shot view.
(2) Noisy Label Refinement. For samples identified as noisy, we apply a dual-modal consistency strategy to selectively correct their labels, leveraging alignment between visual and textual modalities to guide refinement while minimizing reliance on potentially biased internal knowledge.
(3) Model Adaptation. The model is progressively fine-tuned using the jointly curated dataset of selected clean samples and corrected noisy samples, promoting robust adaptation to the target task.
Extensive experiments on nine benchmark datasets (both synthetic and real-world noise) demonstrate that DKAF consistently outperforms state-of-the-art multimodal noisy label learning methods. Notably, under high-noise conditions, DKAF achieves average accuracy improvements of 3.28\%.

Mitigating Endogenous Confirmation Bias in Noisy Label Learning for Vision-Language Models

Self-interpretable models are increasingly valued for their inherent explainability. Among them, part-prototype networks stand out by mimicking human reasoning through the use of learned prototypes. However, their explanations often lack stability, becoming sensitive to subtle input perturbations. In this work, we propose Prototype in Imagery Network (PINet), a framework that improves the stability of prototype-based explanations. Rather than training on all possible input variations, which is computationally infeasible, PINet draws inspiration from visual mental imagery. Specifically, we incorporate empty inputs and apply coarse location guidance to simulate the human ability to imagine rough object features (a process akin to Phantasia). PINet mimics this process by incorporating empty inputs and applying coarse location guidance. These imagined, or uncertain, representations are contrasted with those derived from actual inputs (certain representations). We model the differences between the two by computing similarity at both the feature and prototype levels, allowing uncertainty to be explicitly encoded during prototype learning. Comprehensive evaluations on CUB-200-2011 and Stanford Cars demonstrate that PINet consistently achieves robust accuracy and localization, even under noisy conditions. These results represent the ability of PINet to produce stable and interpretable explanations under uncertainty.

PINet: Improving the Stability of Prototype Networks via Phantasia-Inspired Uncertain Representations

Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table.
To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions.
Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

WikiMAG: A Multi-Agent Guided Framework for Generating Structured Wikipedia-like Articles

Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive generalization on vision-language tasks by aligning images and short texts. However, its inherent 77-token length constraint limits the capacity to capture complex semantics in long captions. Existing long-text adaptations for CLIP typically rely on either multi-stage training or truncation-based alignment, both inevitably resulting in semantic degradation and cumbersome tuning. Therefore, we propose One-Stage Long-Text Adaptation for CLIP (OneLIP), a unified framework that extends CLIP to understand long captions within a single training stage, eliminating the need for brittle truncation or multi-stage pipelines. OneLIP addresses semantic degradation by introducing two key innovations: (1) Token Refinement and Importance-guided Modeling (TRIM) module, which selects and refines informative tokens via SVD-based contribution scoring and cross-modal relevance modeling; (2) Per-sample Online Hard Negative Mining (PO-HNM) strategy dynamically maintains sample-specific negatives based on dual-consistency difficulty tracking, which is superior to other strategies in long-text scenarios where key semantics are distributed in more scattered positions. Extensive experiments on long-text image retrieval, short-text image retrieval, zero-shot classification, and text-to-image generation demonstrate OneLIP's robustness and versatility across diverse input lengths, offering a semantically faithful solution for long-text adaptation for CLIP.

OneLIP: Unlocking and Improving Long-Text Representations of CLIP via One-Stage Adaptation

Assessing enterprise resilience under uncertainty necessitates capturing both intrinsic attributes and evolving inter-enterprise dependencies. However, real-world enterprise systems pose substantial structural challenges: redundant or loosely correlated links can trigger spurious relational inferences, while missing or latent dependencies often hinder the propagation of informative signals. Moreover, most existing approaches adopt static graph priors or decouple structural refinement from semantic learning, lacking a co-evolutionary paradigm that allows structure and representation to inform one another. We propose **CFU**, a novel **C**o-evolving **F**ramework under **U**ncertainty, which reconceptualizes graph structure as a dynamic and learnable component evolving alongside node semantics. CFU begins with a structure-aware contrastive pretraining phase to distill latent relational semantics without supervision. It then performs bidirectional structural refinement, filtering structurally redundant edges through semantic agreement scoring, and uncovering temporally contingent, task-relevant dependencies via similarity-guided inference. These operations are integrated through a dynamic fusion process that continuously aligns the evolving topology with the resilience objective. By embedding structural adaptation within the learning loop, CFU enables context-aware resilience assessment across incomplete, ambiguous, and structurally volatile enterprise environments. Extensive experiments on real-world datasets demonstrate its superior performance across diverse evaluation scenarios.

Beyond Graph Priors: A Co-Evolving Framework Under Uncertainty for Enterprise Resilience Assessment

World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environments that are high-dimensional, non-stationary, and composed of multiple objects with rich interactions since most world models learn holistic representations of all environmental components. By contrast, humans perceive the environment by decomposing it into discrete objects, facilitating efficient decision-making. Motivated by this insight, we propose *Slot Transformer Imagination with CAusality-aware reinforcement learning* (STICA), a unified framework in which object-centric Transformers serve as the world model and causality-aware policy and value networks. STICA represents each observation as a set of object-centric tokens, together with tokens for the agent action and the resulting reward, enabling the world model to predict token-level dynamics and interactions. The policy and value networks then estimate token-level cause--effect relations and use them in the attention layers, yielding causality-guided decision-making. Experiments on object-rich benchmarks demonstrate that STICA consistently outperforms state-of-the-art agents in both sample efficiency and final performance.

Object-Centric World Models for Causality-Aware Reinforcement Learning

Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal reasoning capabilities and such hallucinations. However, existing reasoning approaches in LLMs, such as Chain-of-Thought (CoT) and its graph-based variants, operate at the linguistic token level rather than modeling the underlying causal relationships between variables, lacking the ability to represent conditional independencies or satisfy causal identification assumptions. To bridge this gap, we introduce causal-DAG construction and reasoning (CDCR-SFT), a supervised fine-tuning framework that trains LLMs to explicitly construct variable-level directed acyclic graph (DAG) and then perform reasoning over it. Moreover, we present a dataset comprising 25,368 samples (CausalDR), where each sample includes an input question, explicit causal DAG, graph-based reasoning trace, and validated answer. Experiments on four LLMs across eight tasks show that CDCR-SFT improves the causal reasoning capability with the state-of-the-art 95.33% accuracy on CLADDER (surpassing human performance of 94.8\% for the first time) and reduces the hallucination on HaluEval with 10% improvements. It demonstrates that explicit causal structure modeling in LLMs can effectively mitigate logical inconsistencies in LLM outputs. Code at https://github.com/MrLYG/CDCR-SFT.

Mitigating Hallucinations in Large Language Models via Causal Reasoning

Although Generative Flow Networks (GFlowNets) are designed to capture multiple modes of a reward function, they often suffer from mode collapse in practice, getting trapped in early-discovered modes and requiring prolonged training to find diverse solutions. Existing exploration techniques often rely on heuristic novelty signals. We propose Loss-Guided GFlowNets (LGGFN), a novel approach where an auxiliary GFlowNet's exploration is directly driven by the main GFlowNet's training loss. By prioritizing trajectories where the main model exhibits high loss, LGGFN focuses sampling on poorly understood regions of the state space. This targeted exploration significantly accelerates the discovery of diverse, high-reward samples. Empirically, across diverse benchmarks including grid environments, structured sequence generation, Bayesian structure learning, and biological sequence design, LGGFN consistently outperforms baselines in exploration efficiency and sample diversity..For instance, on a challenging sequence generation task, it discovered over 40 times more unique valid modes while simultaneously reducing the exploration error metric by approximately 99\%.

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