reasoning
reasoning on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on reasoning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around reasoning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Complete Guide to Thinking Machines Inkling
Thinking Machines Lab’s Inkling represents a significant advancement in AI foundation models. This open-weights model, boasting 975B parameters and a 1M-token context window, prioritizes adaptability over benchmark scores. Designed as a customizable base for diverse applications—from multimodal reasoning and agentic AI to coding and audio-visual tasks—Inkling empowers developers to build specialized solutions. Explore the complete guide to understand Inkling's architecture and potential. For broader context on the evolving AI landscape, consider "What to watch for after Jensen Huang’s Japan visit."
![Seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture (preprint + code) [R]](https://preview.redd.it/b0u6q9a46ndh1.jpg?width=140&height=98&auto=webp&s=dbb02d2e0fc85305a04e37864167c2578891d46c)
Seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture (preprint + code) [R]
Researchers have introduced DABSN (Dynamic Adaptive Bias State Network), a novel recurrent language model architecture demonstrating promising results in reasoning, memory, and long-sequence tasks. The initial preprint and accompanying code—available in PyTorch, C++, and Triton—detail the architecture’s behavior and performance across benchmarks like MQAR and A5/60. Early language modeling experiments with a 24M parameter model have yielded unexpectedly strong results, prompting a second paper focused on scaling and long-context behavior. Collaboration is sought for independent reproduction, evaluation design, and access to larger GPU resources.
Agents think in milliseconds, legacy infrastructure doesn't. LinkedIn, Walmart and Zendesk shared how they closed the gap at VB Transform 2026
Agents operate at lightning speed, but legacy infrastructure often lags behind. A key takeaway from VB Transform 2026 was clear: the real bottleneck in AI agent deployment isn't the models themselves, but rather the underlying infrastructure. LinkedIn, Walmart, and Zendesk shared their experiences navigating this challenge, highlighting the need for a shift from human-centric systems to those optimized for agentic workflows. Discover how these leaders are building for model and context independence to unlock greater productivity and innovation.
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
The conversation around loop engineering often centers on Large Language Models (LLMs), but can the architecture itself drive improved performance? This article presents a novel experiment, rigorously testing a deterministic, zero-dependency Python benchmark to isolate failures—without an LLM. Results across 300 random seeds demonstrate that goal-directed controllers consistently outperform linear pipelines in completing independent branches. Explore the architecture, benchmark, and debugging process, revealing that failure isolation is, in fact, a measurable property of control flow.
You can build your AI's memory just by talking. Here's the catch. #AI #aiagents #AImemory
Unlock your AI agent's potential with a surprisingly simple approach: conversational memory. You can build it just by talking. The catch? Scaling this memory effectively reveals underlying architectural complexities that can slow development. Prioritizing a robust context store, as explored in our article "Comprehension at AI Speed," is crucial for maintaining agility and preventing hidden bottlenecks. #AI #aiagents #AImemory