The Archive

BrainJanus unifies brain encoding, vision, and language in single multimodal model with brain tokenizer for neural-sensory correspondence.

The startup, Proception, is taking a unique approach to collecting training data to tackle one of the hardest problems in robotics: hands.

Industry experience paper catalogs five MCP server architectural patterns across 15 production implementations post-Anthropic November 2024 release.

Reinforcement learning controller for workload shifting in wind-integrated HPC data centers; RL shows credit-assignment problems in minimal test case.

TRACE: RANO 2.0-aligned concept bottleneck model for interpretable 4-class glioblastoma response classification on longitudinal 3D MRI.

DialogPII: multilingual synthetic dialog dataset across eight scenarios for personal information detection and de-identification system evaluation.

CDF-based Sliced Wasserstein distance estimators eliminate sorting requirements and improve data parallelization for optimal transport computation.

Survey of persistent-state LLM agents covering memory, credentials, audit records, and lifecycle governance across six diagnostic axes.

UKRI metascience project compares GPT-4o, Mistral, and DSIT-Taxonomies for research entity extraction from 42 funding proposal abstracts.

ManimAgent: self-evolving multimodal agent with episodic memory bank carries reflection experience across code-generation tasks for scientific visualization.

Rhetor: multi-agent system automates live product demos via web app control, real-time QA, and source-code awareness to replace human presenters.

DreamForge-World 0.1: low-compute world model for real-time interactive simulation on consumer GPUs with keyboard/mouse control and multimodal init.

PromptGNN-sim: bidirectional GNN-LLM fusion framework for text-attributed graph learning via Graph Attention Networks and semantic-structure alignment.

LoRA variants for continual motion-language agents: addresses stability-plasticity tradeoff in bidirectional motion understanding/generation under sequential task learning.

Theory of acceptance in speculative decoding: formalizes greedy/relaxed verification regimes beyond exact distribution preservation for practical LLM inference.

Dual-Reference SFT defends against embedded harmful supervision in benign fine-tuning data via token-level DPO-style contrastive regularization.

Multi-agent system using open-source LLMs for automated disinformation detection and fact-checking at scale across social media.

KnowsTFM: knowledge-graph-informed fine-tuning improves small tabular foundation models in niche, high-dimensional, low-data domains.

EMPATH: multilingual multi-turn safety benchmark for emotional-support chatbots across 140 scenarios, 34 personas, and 19 metrics on crisis handling.

Import AI 463 covers self-improving robots, a 10k-GPU Chinese cluster deployment, and commentary on AI's societal transition.

Inoculation adapters: LoRAs that suppress undesired capability generalization via three-stage training and disposal, reducing emergent misalignment backdoors.

Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.

Unsupervised community detection in heterophilic graphs using discrete Ricci curvature and diffusion.

Text-to-video diffusion models leverage data manifold structure as implicit reward signal without external annotations.

Theoretical framework for grounding LLM reasoning with incomplete knowledge graph evidence using entity anchors and path energies.

Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infrastructure that coordinates projects, agents…

In our goal to develop personalised dysarthric speech recognition (DSR) models, this study compared the recognition performances of human listeners and those of three state-of-the-art, off-the-shelf ASR systems (Whisper-large-V3, Google Chirp 3, and Omnilingual) on the…

Medication errors, particularly dosing errors in clinical trials (CT), can lead to patient harm, adverse drug events and worse patient outcomes. Dosing errors are preventable, and early identification can improve trial integrity and mitigate subsequent clinical and financial…

Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training. Distributionally robust optimization (DRO) addresses this by…

Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO). Existing batch BO methods, however, incur large computational cost or rely on approximations that erode batch diversity. We…