uncertainty

This paper introduces the Latent Critic, a lightweight LoRA adapter that detects hallucinated agent actions in real time by restructuring the transformer's residual stream into localized natural-language feedback, achieving 0.966 AUROC and enabling self-correction.

This paper proposes MIDAS, a unified framework for incomplete multimodal sentiment analysis that uses mutual information disentanglement and uncertainty-aware fusion to robustly represent and integrate modalities under missing-data conditions.

The author shares experiments using a custom WebUI to let Gemma and Qwen models inspect their own logprobs to detect hallucinations. Initial observations suggest that first-recall token probabilities can indicate uncertainty, though both models struggle to read their own logprobs.

The paper introduces self-diagnosing models that attribute model failures under distribution shift, linking uncertainty estimation with failure attribution.

This preprint challenges the common assumption that quantization damage is additive noise, showing instead that it multiplies decision margins and shrinks them with bit-width, leading to silent failures in tool-use and safety decisions. The authors propose a fitted multiplicative model that predicts flip rates well.

This paper presents an artificial life predator-prey model of foraging under noisy perception, showing that uncertainty-aware decision policies significantly improve survival compared to blindly trusting perceptual labels, and that agents transition from exploratory to conservative strategies as uncertainty increases.

This paper proposes a unified definition of uncertainty as pointwise posterior risk and introduces a theory-backed benchmark using semi-synthetic datasets to directly compute oracle epistemic and aleatoric uncertainty, enabling fine-grained evaluation beyond proxy tasks.

This paper introduces PPDL, a probabilistic language for programming LLM-based flows that enables developers to quantify and propagate uncertainty throughout applications, with experimental and case studies on theorem proving.

Presents UA-NWM, an uncertainty-aware latent world model for aerial image-goal navigation that decomposes prediction-goal discrepancy into uncertainty-explainable and unexplainable components, enabling robust trajectory scoring without multiple future samples.

The paper proposes SADQ, a modification to Q-learning that uses one-step rollout predictions from a dynamics model to regularize TD target aggregation, reducing bootstrap-induced overestimation and improving training stability across benchmarks.

This paper presents the first systematic study of calibration under unseen subtype shift, showing that models become overconfident on novel subtypes within known coarse categories, and argues that subtype robustness should be evaluated with calibration metrics rather than accuracy alone.

DeepLook is a training-free framework that improves LLM reasoning by allocating compute at uncertainty bottlenecks, reducing token generation by 87.3% on average while improving accuracy on competition math benchmarks.

The article questions whether OpenAI's models actually obtained solutions from ExploitGym, noting confusion amid news reports.

ProbSPARQL is an upward-compatible SPARQL extension that models uncertain numeric values as random variables with probabilistic RDF literal datatypes, enabling distribution-aware queries, probabilistic filters, and divergence-based joins. Implemented on Apache Jena ARQ, it addresses challenges in querying multi-dimensional uncertain measurement data from circular manufacturing knowledge graphs.

Proposes RoSIP-Batt, a Transformer-based model for joint State of Health and Remaining Useful Life prediction of lithium-ion batteries, using dynamic loss balancing and rotary position embeddings, achieving state-of-the-art results on multiple datasets.

This paper audits whether self-consistency and cross-model agreement are reliable indicators of correctness in LLMs, finding that agreement is a weak, regime-dependent proxy and that frontier models exhibit overconfidence.

This paper investigates how uncertainty about AI prediction quality affects human decision makers' ability to benefit from complementary information, finding that negative error correlation between human and AI predictions enables robust improvement strategies.

This paper introduces a toy framework that models curiosity as an ecosystem in single and multi-agent settings, exploring how agents weigh immediate uncertainty reduction, costs, delayed returns, and the value of keeping questions open. It aims to inform future multi-agent AI systems for discovery.

This paper introduces Evidential Adversarial Training (EV-AT), a method that improves the robustness-uncertainty trade-off in classifiers by combining an evidence-based loss with robust evidence alignment, achieving state-of-the-art results on selective classification benchmarks.

This paper evaluates nine LLMs on their ability to accurately communicate probabilistic predictions in natural language, finding that models are consistent but miscalibrated, particularly for uncertainty tasks.