Singapore

Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models based on similarity metrics such as cosine distance. Hard negative mining becomes challenging for biomedical and scientific domains due to the difficulty in distinguishing between source and hard negative documents. However, referenced documents naturally share contextual relevance with the source document but are not duplicates, making them well-suited as hard negatives. In this work, we propose BiCA: Biomedical Dense Retrieval with Citation-Aware Hard Negatives, an approach for hard-negative mining by utilizing citation links in 20,000 PubMed articles for improving a domain-specific small dense retriever. We fine-tune the GTE_small and GTE_Base models using these citation-informed negatives and observe consistent improvements in zero-shot dense retrieval using nDCG@10 for both in-domain and out-of-domain tasks on BEIR and outperform baselines on long-tailed topics in LoTTE using Success@5. Our findings highlight the potential of leveraging document link structure to generate highly informative negatives, enabling state-of-the-art performance with minimal fine-tuning and demonstrating a path towards highly data-efficient domain adaptation.

AAAI 2026

BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives

citation-aware

biomedical dense retrieval

hard negative mining

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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.

Motivated by applications in forecasting, we study chronological reasoning in LLMs. We test LLMs’ ability to understand and enforce chronological order in three types of tasks : sorting randomly shuffled historical events; conditional sorting of events defined by some conditions; and anachronism detection based on intersections of multiple timelines. Our experiments use events that we first confirm are known to the LLM; this ensures that we test chronological understanding on an LLM’s pretrained internal knowledge. Across three LLM families— GPT-4.1 (standard), GPT-5 (hybrid-reasoning), and Claude 3.7 Sonnet (large-reasoning, with and without Extended Thinking), we find that performance degrades rapidly with problem complexity but improves greatly for reasoning models with test-time extended reasoning.

Do Large Language Models (LLMs) Understand Chronology? (Student Abstract)

Proactive dialogue systems, which are designed to guide
conversations toward predetermined goals. However,
contemporary LLMs predominantly function as passive
assistants, mechanically executing human instructions. A
key challenge contributing to this limitation is the
inherent difficulty in acquiring and annotating
high-quality training data for proactive dialogue.
Consequently, the scarcity of such data results in a
notable deficiency in the proactive conversational
capabilities of current LLMs.In this paper, we introduce
PANDA (Proactive Agent-based Negotiation Dialogue
Augmentation), a method designed to generate accurate,
complex, and diverse proactive dialogue data for a
challenging task—financial dispute mediation—where a LLM
acts as the mediator. PANDA leverages a novel self-evolving
synthesis process to manage a pool of user profiles and
generate dialogues through structured interactions between
multiple LLM-driven agents. To ensure data fidelity, we
propose a comprehensive evaluation framework and build a
two-level validation system combining automated and expert
human verification. Our experiments demonstrate that an
8B-parameter model, trained on our synthesized dataset,
achieves state-of-the-art results in the task's evaluation
framework. Its performance rivals top closed-source models
guided by heavily engineered prompts, even when provided
with only essential information.

PANDA: Empowering Small Language Models for Proactive Dialogue Through Agent-Based Synthesis (Student Abstract)

Adversarial training is an effective technique for
enhancing the robustness of deep neural networks (DNNs).
Prior research shows that misclassified examples influence
final adversarial robustness much more than correctly
classified examples. Ignoring this difference during
training can hurt model performance. In crowdsourcing,
varying annotator expertise causes noisy, inconsistent
labels. As a result, it is hard to distinguish
misclassified and correctly classified examples using only
provided annotations. Thus, how to use the reliability and
discrepancy between these example types to improve
robustness within adversarial learning remains a critical
but underexplored issue.
In this work, we first explore how misclassified and
correctly classified examples affect learning from crowds
(LFC) in adversarial environments. Then, we formulate the
problem of misclassification-aware robust learning from
multiple human labelers as a bilevel min-max problem. After
that, we introduce MALC, a new approach to make classifiers
more robust to adversarial examples via iterative
adversarial example generation and parameter estimation. We
conduct an extensive evaluation of the proposed MALC,
showing that MALC can outperform the state-of-the-art LFC
methods in both white-box and black-box settings.

Misclassification-Aware Robust Learning from Multiple Human Labelers (Student Abstract)

Linear models are widely used in high-stakes
decision-making due to their interpretability, but fairness
constraints like Demographic Parity (DP) create opaque
effects on model coefficients and predictive bias
distribution. We propose a post-processing framework that
can be applied on top of any linear model to decompose bias
into direct (sensitive-attribute) and indirect
(correlated-features) components. Our method analytically
characterizes how DP reshapes each coefficient, enabling
transparent feature-level interpretation.

Decomposing Direct and Indirect Biases in Linear Models Under Demographic Parity Constraint (Student Abstract)

Due to their ability of follow natural language
instructions,
vision-language-action (VLA) models are increasingly preva-
lent in the embodied AI arena, following the widespread suc-
cess of their precursors—LLMs and VLMs. In this paper,
we discuss 10 principal milestones in the ongoing develop-
ment of VLA models—multimodality, reasoning, data, eval-
uation, cross-robkot action generalization, efficiency,
whole-
body coordination, safety, agents, and coordination with hu-
mans. Furthermore, we discuss the emerging trends of us-
ing spatial understanding, modeling world dynamics, post
training, and data synthesis—all aiming to reach these mile-
stones. Through these discussions, we hope to bring
attention
to the research avenues that may accelerate the development
of VLA models into wider acceptability.

10 Open Challenges Steering the Future of Vision-Language-Action Models

Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support long-term temporal modeling, large-scale spatial coverage, and multi-source drivers remain scarce. To address this gap, we present a 25-year, daily-resolution wildfire dataset covering 39 million hectares across British Columbia and surrounding regions. The dataset includes 39 covariates, encompassing active fire detections, weather variables, fuel conditions, terrain features, and anthropogenic factors. Using this benchmark, we evaluate a diverse set of time-series forecasting models—including CNN-based, linear-based, Transformer-based, and Mamba-based architectures—and systematically investigate key challenges such as the role of long temporal context, effectiveness of multi-task learning, and the relative importance of different fire-driving factors. All data and code are made publicly available in the supplementary material to facilitate future research in both climate-related hazard prediction and time series forecasting.

BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction

In-context learning (ICL) has proven to be adept at adapting large language models (LLMs) to downstream tasks without parameter updates, based on a few demonstration examples.
Prior work has found that the ICL performance is susceptible to the selection of examples in prompt and made efforts to stabilize it.
However, existing example selection studies ignore the ethical risks behind the examples selected, such as gender and race bias.
In this work, we conduct extensive experiments and discover that (1) example selection with high accuracy does not mean low bias; 
(2) example selection for ICL may amplify the biases of LLMs;
(3) example selection exacerbates some spurious correlations of LLMs.
To mitigate the bias amplification caused by selected examples, we propose Remind with Bias-aware Embedding (*ReBE*), which disrupts spurious correlations through contrastive learning and learns bias-aware embedding for LLMs via prompt tuning.
Finally, we demonstrate that ReBE effectively mitigates biases of LLMs without significantly compromising accuracy and is highly compatible with existing example selection methods.

The Silent Amplifier: In-Context Examples Fuel Bias in Large Language Models

Recent vision-language-action (VLA) models built on pretrained vision-language models (VLMs) have demonstrated strong performance in robotic manipulation. However, these models remain constrained by the single-frame image paradigm and fail to fully leverage the temporal information offered by multi-frame histories, as directly feeding multiple frames into VLM backbones incurs substantial computational overhead and inference latency. We propose CronusVLA, a unified framework that extends single-frame VLA models to the multi-frame paradigm. CronusVLA follows a two-stage process: (1) Single-frame pretraining on large-scale embodied datasets with autoregressive prediction of action tokens, establishing an effective embodied vision-language foundation; (2) Multi-frame post-training, which adapts the vision-language backbone’s prediction from discrete tokens to learnable features, and aggregates historical information via feature chunking. CronusVLA effectively addresses the existing challenges of multi-frame modeling while enhancing performance and observational robustness. To evaluate the robustness under temporal and spatial disturbances, we introduce SimplerEnv-OR, a novel benchmark featuring 24 types of observational disturbances and 120 severity levels. Experiments across three embodiments in simulated and real-world environments demonstrate that CronusVLA achieves state-of-the-art performance and superior robustness, with a 70.9\% success rate on SimplerEnv, a 17.2\% improvement over OpenVLA on LIBERO, and the highest robustness score on SimplerEnv-OR. These results highlight the potential of efficient multi-frame adaptation in VLA models for more powerful and robust real-world deployment.

Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling

A few pixel attack misleads a classifier by modifying a few pixels of an image. Its perturbation space is an $\ell_0$-ball, which is not convex, unlike $\ell_p$-balls for $p\geq1$. However, existing local robustness verifiers typically scale by relying on linear bound propagation, which captures convex perturbation spaces. We show that the convex hull of an $\ell_0$-ball is the intersection of its bounding box and an asymmetrically scaled $\ell_1$-like polytope. For single channel inputs, the volumes of the convex hull and this polytope are nearly equal as the input dimension increases. We then show a linear bound propagation that precisely computes bounds over the convex hull and is significantly tighter than bound propagations over the bounding box or our $\ell_1$-like polytope. This bound propagation scales the state-of-the-art $\ell_0$ verifier on robustness benchmarks by 1.24x-7.07x, with geometric mean of 3.16.

Tight Robustness Certification Through the Convex Hull of ℓ₀ Attacks

Future direct-imaging flagship missions, such as NASA's Habitable Worlds Observatory (HWO), face critical decisions in prioritizing observations due to extremely stringent time and resource constraints. In this paper, we introduce two advanced machine-learning architectures tailored for predicting biosignature gas fluxes from exoplanetary reflected-light spectra: a Bayesian Convolutional Neural Network (BCNN) and our novel model architecture, the Spectral Query Adaptive Transformer (SQuAT). The BCNN robustly quantifies both epistemic and aleatoric uncertainties, offering reliable predictions under diverse observational conditions, whereas SQuAT employs query-driven attention mechanisms to enhance interpretability by explicitly associating spectral features with specific biosignature gases. We demonstrate that both models achieve comparably high predictive accuracy on an augmented dataset spanning a wide range of exoplanetary conditions, while highlighting their distinct advantages in uncertainty quantification and spectral interpretability. These capabilities position our methods as promising tools for accelerating target triage, optimizing observation schedules, and maximizing scientific return for upcoming flagship missions such as HWO.

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Do Large Language Models (LLMs) Understand Chronology? (Student Abstract)

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