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Pulse · newsbytesapp.com

newsbytesapp.com · Nov 4

Sarvam's new AI model makes decisions in just 44 milliseconds

Pulse · kashmirdespatch.com

kashmirdespatch.com · Nov 4

IndiaAI Mission Released Indigenous Foundation Models EKA-1 by Soket and Dhīr 2B & Dhīr 5B by ZenteiQ Under the IndiaAI Innovation Centre Pillar

Vinod Bhat New Delhi: The IndiaAI Mission, under the Ministry of Electronics and Information Technology (MeitY), today released the indigenous foundation models EKA-1 and Dhīr (Dhīr 2B & Dhīr 5B) developed by Soket Labs Technology and Research Private Limited and ZenteiQ AiTech Innovations re

Pulse · news.lavx.hu

news.lavx.hu · Nov 4

IndiaAI Mission Unveils Three Indigenous AI Models As Soket Launches EKA-1 And ZenteiQ Releases Dhīr 2B And 5B

Anthropic released FlashProve, an open-source zkSNARK system that verifies large language model responses came from a specific set of weights. The tool proves inference on a 400-billion-parameter model at 110 tokens per second on a single H100.

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arXiv

Nov 3

DreamX: Video Diffusion Model is a Universal Latent Action Learner

Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model's rich spatiotemporal features, compressing each latent video frame by $\boldsymbol{300\times}$ ($\boldsymbol{1{,}200 \rightarrow 4}$ tokens). In addition, by leveraging the video model's generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent…

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arXiv

Sep 18

LogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers

Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely rely on outcome-based feedback, leaving the logical validity of intermediate reasoning steps largely unverified. To address the…

arXiv

Sep 18

Benchmarking Gender Bias in Machine Translation Evaluation Metrics across Occupations

Gender bias remains a persistent concern in machine translation (MT), affecting both generated translations and their automatic evaluation. When a source text leaves a person's gender unspecified, translations may realize that person using masculine or feminine forms, and both…

arXiv

Sep 18

Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving

Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene representations. Incomplete evidence leads to low-quality candidate generation and unreliable ranking among geometrically similar…

arXiv

Sep 18

HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference

Homomorphic encryption (HE) has emerged as a promising approach to privacy-preserving machine learning (PPML), enabling computation directly over encrypted data. In HE-based PPML, a client submits an encrypted input to the server, which evaluates models such as large language…

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