AI Research

Researchers introduced NEST-V1, a multimodal framework that generates emotion-conditioned Nepali Sign Language avatars from spoken words, achieving 81.1% ASR accuracy and 79.21% emotion recognition accuracy on a dataset …

Researchers developed a methodology to transform Hindi WordNet into 1.25 million instruction-response pairs, fine-tuning a 12B-parameter language model to create a specialized conversational AI for low-resource languages…

Researchers present the first systematic survey of unified vision-language perception in Multimodal Large Language Models (MLLMs), tracing the paradigm evolution through a five-stage taxonomy and identifying open challen…

Researchers propose AnySimLite, a lightweight similarity encoder for on-device speech-adjacent classification that achieves state-of-the-art or competitive performance in few-shot settings while using less than 1/250th t…

Researchers propose SOLAR, an auxiliary objective for supervised fine-tuning that aligns soft-token representations across languages using English as a pivot, improving cross-lingual reasoning in multilingual large langu…

Researchers propose DocArena, a fully automated pipeline that transforms raw document collections into controllable training environments for document search agents, using multimodal large language models to structure do…

Researchers propose VIGIL, a reinforcement-learning post-training framework that reduces hallucinations in multimodal large language models by maximizing mutual information between visual input and generated responses. T…

Researchers developed a multimodal deep learning model for methane-plume segmentation from hyperspectral satellite imagery, integrating a feature-guided methane enhancement mechanism into transformer-based RGB representa…

Researchers propose ForeAgent, an agentic forensics framework for AI-generated image detection that uses a Perception-Verdict architecture and a Hindsight-Driven Self-Refining strategy to iteratively improve. The system …

Researchers propose a lattice-theoretic method for AI oracles to report unbiased, self-consistent probability sets, using the Knaster-Tarski fixed-point theorem to select a canonical credal set that avoids the self-refer…

Researchers developed topology-informed neural networks that integrate topological data analysis with deep learning to improve flood detection in satellite imagery, achieving more robust and interpretable results on the …

Researchers introduced Chisao, a GPU-native parallel optimizer for multimodal black-box functions that uses a convergence-anticonvergence oscillation cycle to escape local traps while freezing confirmed modes. On the Sim…

Researchers introduced Hankel Reduced order Model (HRM) adapters, a state space model-based fine-tuning method that outperforms LoRA on long-context tasks like QuALITY and QMSum by leveraging FFT-based parallel scan for …

Researchers introduced KG-TRACE, a neuro-symbolic framework that integrates the WHO mutation knowledge graph as a biological constraint on neural genomic models for antimicrobial resistance prediction. Evaluated on the C…

A new thesis on algorithmic fairness argues that current fairness paradigms rely on deterministic point estimates and treat individuals as isolated entities, limiting their effectiveness in complex socio-technical enviro…

Researchers developed a general Graph Neural Network framework that learns algebraic properties of finite groups, such as abelianity, nilpotency, and solvability, directly from their Cayley graph representations. The fra…

Researchers closed a gap in high-probability bounds for stochastic gradient descent under the Polyak-Łojasiewicz condition with Markovian noise, proving optimal linear dependence on mixing time. They also extended the fr…

Researchers propose the Drift-Diffusion-Enhanced Elo Rating System (DD-Elo), a new chess skill assessment framework that integrates move-level data to adapt to skill changes faster than traditional Elo. The system, inspi…

Researchers propose Fast LeWorldModel (Fast-LeWM), a fast latent world model that replaces repeated local rollout with action-prefix prediction, improving average success over LeWM while substantially reducing planning t…

Researchers introduced a practical Dataset Usage Inference (DUI) framework that estimates what fraction of a dataset was used to train a machine learning model without requiring shadow models or held-out data. The method…