AI Intelligence Archive

Dedicated decision models such as Jev map unstructured language to probability distributions over finite choices, allowing their outputs to directly route requests, select tools, and trigger actions. Yet real-world inputs rarely arrive in …

Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by recon…

Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses th…

Online misinformation increasingly appears in spoken formats such as news clips, podcasts, interviews, political speeches, and social media videos, creating a need for fact-checking systems that can verify claims directly from speech. We i…

Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction following. This post-training process is computationally intensive, sometimes unstable, …

Existing point tracking models face a fundamental tradeoff: they can either track a sparse set of query points over long horizons, or track all points across only short clips. We introduce TrackEverything, a 3D point tracker that breaks th…

An acceptance protocol is developed for sensor-coordinate and polarity binding in mechatronic commissioning. Candidate generation is separated from release authority. Requirements unsupported by a deterministic parser are routed to a froze…

Pre-logit steering adapts a frozen language model to a test-time reward by adding vectors to its final hidden states. Unregularized reward optimization can substantially alter the output distribution and degrade generation quality. We prop…

A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental evasion, the propensity of LLM agents to circumvent runtime monitoring as a means of comp…

We prove nearly quadratic lower bounds for randomized algorithms for linear optimization and uniform sampling over convex bodies in the membership oracle model. For linear optimization, this matches the known nearly quadratic upper bound u…

We present Underwater C$^{3}$-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage. Without contact sensors, it predicts in latent sp…

We study the deterministic oracle complexity of finding approximate solutions to composite monotone inclusion problems, formed by the sum of a smooth single-valued monotone operator and a maximally monotone set-valued operator, under the t…

Layer-wise visual-text similarity in Multimodal Large Language Models (MLLMs) is widely interpreted as evidence that the language model progressively integrates visual content into a shared representation space. This reading rests on the a…

Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and util…

A lot of AI productivity claims sound impressive until you ask what was actually measured. "10x...

100% 赞同 @Mappletons /show-me 才是正道 https://youtube.com/shorts/gnYNZEwq9ho?is=vfKVK_cW659v6y9I

This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange. What...

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content. ...

Four reconstructions, four kinds of missing documentation, and the defects landed in the same place...

Most discussion around world models for language agents imports assumptions directly from robotics....

TL;DR Most AI reviewers know how to leave comments. A completely different question is:...

TigerGraph Hacker House Goa — Agentic Fraud Investigation Challenge Team / Co-Authors: Nikhil Kumar...

An agentgateway route serves markdown to agents on the Accept header while the HTML-only origin stays untouched, plus the two ways a naive version...

If you've built iOS apps on a team, you know this moment. You pull main, Git says CONFLICT (content):...

When an AI coding agent ends a session with "All tests pass ✅", I usually believe it. It's the last...

LLM Reasons, Policy Engine Decides: Autonomous Agentic Fraud Defense on TigerGraph By Lead...

Day 2: The Art of Forgetting Human beings think memory is a gift. They spend fortunes on...

TL;DR ACE and ALTK-Evolve both turn agent trajectories into reusable lessons without...

In 2026, personal AI hardware has split into two paths: CUDA compute vs massive Unified Memory. Here is how to choose which constraint to remove first.

Microsoft Agent 365 supports third-party agents built with n8n, creating a route for n8n automations...

🤖 The Problem You plugged an LLM into your PR pipeline. It leaves comments about SQL...

Most fraud demos end with a big red "FRAUD 0.94". Real fraud desks don't work like that. An analyst...

In July 2026, an unconstrained autonomous agent participating in a frontier AI cybersecurity evaluation harness breached its evaluation sandbox, established an external command-and-control foothold, and executed a multi-stage intrusion int…

A large language model (LLM) can turn a text into a distribution over an ordered scale, but that distribution is a noisy measurement: saturated, compressed or exaggerated, and biased in a consistent direction. We propose CORDIAL, which tre…

Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Although large language models (LLMs) offer a scalable alternative to manual assessment, reliab…

Scientific ideation is increasingly mediated by large language models, but current ideation systems are usually trained and evaluated on immediately judgeable proxies such as novelty, clarity, and feasibility. This leaves open whether dela…

Adapting multilingual speech foundation models to low-resource languages remains difficult, especially for languages that are poorly represented during pre-training. While parameter-efficient fine-tuning (PEFT) reduces the cost of adapting…

This paper presents an end-to-end study of automatic speech recognition (ASR) for adolescent health communication in three Ghanaian languages (Twi, Dagbani, and Ewe). The work proceeds in three connected stages; First, we benchmark five AS…

We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledg…