· ai
[Paper] Vision-Language Model for Accurate Crater Detection
The European Space Agency (ESA), driven by its ambitions on planned lunar missions with the Argonaut lander, has a profound interest in reliable crater detectio...
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- · ai
[Paper] Kinship Data Benchmark for Multi-hop Reasoning
Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of information into a ...
- · ai
[Paper] Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification
System logs are crucial for monitoring and diagnosing modern computing infrastructure, but their scale and complexity require reliable and efficient automated i...
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[Paper] Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning
LLM agents operating over massive, dynamic tool libraries rely on effective retrieval, yet standard single-shot dense retrievers struggle with complex requests....
- · ai
[Paper] Enhancing Self-Correction in Large Language Models through Multi-Perspective Reflection
While Chain-of-Thought (CoT) prompting advances LLM reasoning, challenges persist in consistency, accuracy, and self-correction, especially for complex or ethic...
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[Paper] OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent
While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current frameworks struggle with robustness in long-horizon workfl...
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[Paper] DT-ICU: Towards Explainable Digital Twins for ICU Patient Monitoring via Multi-Modal and Multi-Task Iterative Inference
We introduce DT-ICU, a multimodal digital twin framework for continuous risk estimation in intensive care. DT-ICU integrates variable-length clinical time serie...
- · ai
[Paper] Beyond External Guidance: Unleashing the Semantic Richness Inside Diffusion Transformers for Improved Training
Recent works such as REPA have shown that guiding diffusion models with external semantic features (e.g., DINO) can significantly accelerate the training of dif...
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[Paper] Are LLM Decisions Faithful to Verbal Confidence?
Large Language Models (LLMs) can produce surprisingly sophisticated estimates of their own uncertainty. However, it remains unclear to what extent this expresse...
- · ai
[Paper] Contrastive Learning with Narrative Twins for Modeling Story Salience
Understanding narratives requires identifying which events are most salient for a story's progression. We present a contrastive learning framework for modeling ...
- · ai
[Paper] Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding
Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reaso...
- · ai
[Paper] Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
Kolmogorov-Arnold Networks (KANs) have shown strong potential for efficiently approximating complex nonlinear functions. However, the original KAN formulation r...