How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
arXiv:2605.23926v1 Announce Type: new
Abstract: Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces reveals extensive reformulation, verification, and circular self-reflect...
Context: Proactive Goal-Directed Intelligence via Composable Sandboxed Programs, Declarative Wiring, and Structured Interaction
arXiv:2605.23928v1 Announce Type: new
Abstract: We present Context, the intelligence layer of the Magarshak Architecture, which replaces reactive query-response chatbots with proactive goal-directed agents that advance shared tasks without waiting for user prompts. The architecture rests on three m...
Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
arXiv:2605.23929v1 Announce Type: new
Abstract: Modern AI systems increasingly rely on workflows composed of multiple interacting agents, some powered by large language models (LLMs) and others by conventional computational modules. This paper analyzes the fundamental tradeoffs between latency, rel...
AI Weekly Issue #496: Anthropic just opened its Pentagon-grade model to everyone
In the past 48 hours: Anthropic released Mythos — its Pentagon and NSA-deployed model — to the general public, resetting what counts as the public frontier. A coordinated SQL-injection campaign weaponised Ghost CMS across 700+ sites including Harvard and Oxford. Meta started cutting 8,000 jobs this ...
Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving
Together AI has released OSCAR (Offline Spectral Covariance-Aware Rotation), an INT2 KV cache quantization method for long-context LLM serving. Unlike prior rotation-based approaches that apply data-oblivious Hadamard transforms, OSCAR derives separate rotations for keys and values from attention-aw...
I Built My First ETL Pipeline as a Complete Beginner. Here’s How.
A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API
The post I Built My First ETL Pipeline as a Complete Beginner. Here’s How. appeared first on Towards Data Science.
The Death of Middle Management: Automation’s Quiet Restructuring of Organizations
A sharp AI Quantum Intelligence op‑ed examining how AI is quietly eliminating middle management by automating coordination, flattening hierarchies, and shifting power from people managers to system architects. Explores the structural, cultural, and strategic implications for modern organizations.
Pope Leo XIV's first encyclical uses AI as a lens to diagnose older problems: concentrated power, eroding democracy, and a tech elite that shapes the world to its own advantage.
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python.
The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search...
Implementing Hybrid Semantic-Lexical Search in RAG
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.
Introducing the Agent Toolkit for Amazon Web Services
It’s like having your own personal expert AWS solutions architect and data engineer rolled into one.
The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science.
WorkOS Releases auth.md: An Open Agent Registration Protocol Built on OAuth Standards
Most web applications still have no structured way for an AI agent to register. auth.md proposes a fix: a Markdown file apps publish at their domain that tells agents which registration flows are supported, which scopes to request, and how to get credentials tied to a real user — without a human fil...
Latent Cache Flow: Model-to-Model Communication Without Text
arXiv:2605.22863v1 Announce Type: new
Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the receiver model. Recent work such as Cache-to-Cache (C2C; Fu et al., 202...
Reading Calibrated Uncertainty from Language Model Trajectories
arXiv:2605.22864v1 Announce Type: new
Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output. Although cheap, it is often miscalibrated. Methods that probe the model's internal act...
FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning
arXiv:2605.22869v1 Announce Type: new
Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during pretraining. As a result, noisy gradients from limited fine-tuning data ...
The Readout Shortcut: Positional Number Copying Dominates Arithmetic CoT Readout in Small Language Models
arXiv:2605.22870v1 Announce Type: new
Abstract: Chain-of-thought (CoT) prompting is necessary for arithmetic in small language models, yet shuffling its steps preserves most performance. What does CoT contribute if not logical sequencing? In three 1-3B instruction-tuned LMs on GSM8K, we isolate the...
BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
arXiv:2605.22866v1 Announce Type: new
Abstract: Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based methods (SHAP), which decompose a coalition value function into per-component marginal contributions and require evaluation of the...
NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic
arXiv:2605.22874v1 Announce Type: new
Abstract: Effectively translating between natural language (NL) and formal logics like Linear Temporal Logic (LTL) requires expertise that limits formal verification's reach in safety-critical development. Template-based approaches sacrifice expressiveness for ...
RMA: an Agentic System for Research-Level Mathematical Problems
arXiv:2605.22875v1 Announce Type: new
Abstract: We present $\textbf{Research Math Agents (RMA)}$, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies centered on competition mathematics or formal theorem proving, RMA targets research-level math...
SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research
arXiv:2605.22878v1 Announce Type: new
Abstract: The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmented and unstructured knowledge organization impedes deep interdisciplinary integration. Current aca...
Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
arXiv:2605.22883v1 Announce Type: new
Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads this unit remains coherent. For agentic systems - where a single user goal may trigger multi-step orc...