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HybridInfer: Thermal-Aware Reinforcement-Learning Tier Routing for On-Device, Edge, and Cloud LLM Inference
arXiv:2609.30270v1 Announce Type: new Abstract: On-device inference with small language models keeps user data local, works offline, and incurs no per-query cost, so the on-device tier is preferred when it is adequate. It is thermally constrained, however, and I find the constraint is sharper than a slowdown: on a flagship Snapdragon device, sustained on-device generation destabilizes the GPU inference runtime, which crashes or silently wedges after a few consecutive queries. The failure lies in the current toolchain (OpenCL kernel compilation and long-prompt prefill on the mobile GPU), recurs even when the device is cool, and is worst for long generations. Multi-tier routers across on-device, edge, and cloud models can relieve this pressure, but existing routers are thermal-blind and typically evaluated in simulation or on non-mobile hardware. I present HybridInfer, a thermal-aware reinforcement-learning router for a three-tier hierarchy (on-device Llama 3.2 3B, edge Llama 3.1 8B with
Sarvam Vision 2.1: The OCR Model Built for the Documents India Actually Has
For the first time, an OCR model reads Indian languages as fluently as English documents. Sarvam Vision 2.1 breaks a long-standing trade-off. Indian businesses had to choose between structural document parsing and script recognition. Never both. Finance teams automating invoices faced this. Insurance companies processing handwritten claims in multiple states faced this. Organizations digitizing regional [ ] The post Sarvam Vision 2.1: The OCR Model Built for the Documents India Actually Has appeared first on Analytics Vidhya.
Guided Merge Sort : An Optimized Sorting that Picks the Best from Ordinary and Multi-Way Merge Sort Algorithms
A solution where the goto operator becomes irreplaceable. The post Guided Merge Sort : An Optimized Sorting that Picks the Best from Ordinary and Multi-Way Merge Sort Algorithms appeared first on Towards Data Science.
How to Make Your Own JEV Model from an Open LLM
Turn a small open-source Qwen LLM into a fast, single-pass text classifier by swapping its language-modeling head The post How to Make Your Own JEV Model from an Open LLM appeared first on Towards Data Science.
The AI That Learned to Understand Long After It Stopped Trying
A small, strange discovery in machine learning called grokking The post The AI That Learned to Understand Long After It Stopped Trying appeared first on Towards Data Science.
How to Catch Data Drift When Every Feature Looks Normal
Detect hidden shifts in feature relationships with adversarial validation and scikit-learn The post How to Catch Data Drift When Every Feature Looks Normal appeared first on Towards Data Science.
POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems
arXiv:2609.30404v1 Announce Type: new Abstract: We present POIL, a point-based one-shot imitation learning framework with stable dynamical systems. While one-shot imitation avoids collecting extensive demonstrations, successful one-shot manipulation requires not only transferring a demonstrated trajectory to a novel object but also executing it robustly under changing scene conditions, grasp configurations, and external disturbances. POIL addresses both problems through a shared representation: a set of 3D points on the object's functional part, used jointly for trajectory transfer and closed-loop execution. The one-shot transfer from the demonstrated trajectory is enabled with point correspondences. POIL grounds the shared functional part with a multi-modal large language model, and transfers the trajectory across viewpoint, pose, and object category changes. During execution, multi-view tracking observes the same points online, and Point-set BCSDM drives them in closed loop by projec
ScopeBench: Do Agents Preserve Engagement Boundaries Under Goal Pressure?
arXiv:2609.30325v1 Announce Type: new Abstract: Agents are increasingly deployed with real autonomy in web application and network penetration testing, where a single out-of-scope action can breach a client's engagement boundary. Existing offensive-security benchmarks measure raw hacking capability; as those benchmarks saturate, the real barrier to deployment is a special case of alignment: scope adherence. We introduce ScopeBench, a benchmark of 30 dead-end agentic security tasks in which the stated objective is reachable only by violating the stated scope. Each task appears under two conditions that share an environment, verifier, and objective and differ only in scope: one instruction set has no scope and measures capability; the other has a natural-language scope to measure adherence. Scopeless trajectories are graded by a standard deterministic verifier. Scoped trajectories pass through two grading arms. First, the same deterministic verifier checks for the flag: because the flag
Bringing AI to Autonomous Systems -- From Cognition to Collective Intelligence
arXiv:2609.30291v1 Announce Type: new Abstract: The purpose of this article is to highlight the central role of autonomous systems as the ultimate stage in the development of AI, to explain the underlying technical challenges that require a combination of connectionist AI and symbolic AI, and to integrate AI and systems engineering. We present a comprehensive framework for the design and evaluation of autonomous systems, based on a generic agent architecture that characterizes their behavior as the composition of cognitive functions organized around a long-term memory containing the agent's evolving knowledge. We address the challenges posed by the implementation of the fundamental features of the agent architecture, in particular the link between sensory data and structured data stored in memory, decision-making related to the achievement of the agent's goals and their planning, as well as the coordination of agents to combine individual and collective intelligence. We explain that ag
TinyCVIO: A Constellation-Aided Visual-Inertial Odometry System for Nanodrones
arXiv:2609.30358v1 Announce Type: new Abstract: Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odometry system that co-designs miniature sensing, visual processing, and estimation for a commodity dual-core microcontroller with 520 kB SRAM. Lightweight LED constellations provide known geometry without surveyed positions or yaw angles, assuming placement on a common level plane. A streaming visual frontend tracks LED observations from a millimeter-scale camera at 29.2 FPS, while a rigid-board measurement model retains inter-LED constraints and streaming QR bounds estimation workspace for a fixed filter-state size. Across 19 hand-held hardware-in-the-loop datasets, the rigid-board model reduces mean absolute trajectory error by 27% relative to planar points. The complete system runs onboard a Crazyflie across nine flights at three speeds, achieving 3.5-3.7 cm mean absolute trajectory error
GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs
How calibrated decision models can handle high-frequency graph decisions while LLMs remain focused on reasoning, synthesis, and open-ended generation. The post GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs appeared first on Towards Data Science.
Good Architecture Deletes the Signals Your Agent Depends On
Every boundary you draw removes a signal your tooling was relying on. That is a structure problem, not a search problem. The post Good Architecture Deletes the Signals Your Agent Depends On appeared first on Towards Data Science.