Aionda

Why Agent Configs Need Deterministic Control Planes

Why reused coding agent config files can become an unmanaged control layer with security and operational risks.

Financial Recommendations Need Explainability Before Cross-Channel Linking

In financial recommendations, linking anonymous web sessions and logged-in app behavior requires explainability and privacy checks before performance gains.

Learning Motion Feasibility Before Costly Planning in Clutter

A study on filtering infeasible motion attempts in cluttered scenes using point-cloud predictors before sampling-based planning.

OpenFinGym Reframes How Financial AI Systems Are Evaluated

OpenFinGym shifts financial AI evaluation from single-task accuracy to workflow-level testing across prediction, trading, and risk.

CommunityJun 27, 20262026-06-27

Physical AI Bottlenecks Start in Supply Chains

Physical AI commercialization depends less on demos than on chip supply, CoWoS packaging, and deployment infrastructure.

SBI Versus MCMC for Rapid Epidemiological Bayesian Inference

A comparison of SBI and MCMC in SECIR epidemiological models, focusing on posterior agreement, speed, and repeated use.

TGHE Rethinks Private Inference for Transaction Graphs

TGHE proposes private graph inference around reusable local structures instead of global graph-dependent costs.

Agent-Driven Iteration Loops for Industrial Recommender Systems

A look at AgentX and the shift from model changes to automating hypothesis, code, experiment, and analysis loops.

CommunityJun 26, 20262026-06-26

How Agentic AI Redefines Enterprise Coding Metrics Today

Enterprise AI value is shifting from single-response quality to long-running workflow execution and review gates.

k-ai-pulse

RoundupJun 26, 20262026-06-26

AI Resource Roundup (24h) - 2026-06-26

A curated link roundup from recently collected official updates and tech news.

Emotion Vectors in Open LLMs and Behavior Control

Examines whether emotion vectors in open-weight LLMs are internal representations or merely correlated signals for behavior.

HiLSVA Reframes Scientific Visualization Agent Control and Oversight

HiLSVA emphasizes plan-first workflows, human oversight, and provenance over full autonomy in scientific visualization agents.

KARLA Rethinks Retrieval During Token Generation for LLMs

KARLA explores retrieving facts during token generation, reframing RAG tradeoffs around noise, latency, cost, and attribution.

Privacy Risks Shift From Models to Agent Operations

Why LLM agent privacy risks arise from data flows, memory, tools, logs, and delegated permissions in operation.

CommunityJun 26, 20262026-06-26

Read AI Investment News by the Verbs First

AI investment news should be read through official verbs and numbers, not AGI narratives. Build, explore, and assess matter.

Rethinking Trust in Video Reasoning Under Visual Corruption

Examines the Blind Trust Problem in video reasoning and a reliability-based strategy for frame and tool selection.

When AI Can Automate Psychology Experiments Reliably

How trustworthy is AI-run psychology automation? Focus on theory coding, data quality control, and replication limits.

CommunityJun 25, 20262026-06-25

Can 3D Layout Plus AI Improve Animation Stability

Examines whether fixing 3D layout and pose before AI stylization improves animation stability, despite flicker and edit costs.

k-ai-pulse

RoundupJun 25, 20262026-06-25

AI Resource Roundup (24h) - 2026-06-25

A curated link roundup from recently collected official updates and tech news.

Autodata Reframes Synthetic Data as Agentic System Design

Autodata treats synthetic data as an agentic system, raising key questions on validation, leakage, and repeatability.

Automating Benchmarks for Neural Relational Reasoning Generalization

Why automated LLM-built benchmarks for relational reasoning need difficulty control, reliable answers, and bias checks.

CommunityJun 25, 20262026-06-25

Balancing AI Benefits and Existential Risks Economically

Why AI's growth benefits and existential risks should be compared within one economic framework, not separate debates.

Evaluating VLM Visual Search Beyond Accuracy and Tokens

A framework for evaluating VLM visual search with classic human tasks, using token length and search cost beyond accuracy.