Enterprise AI

TL;DR Most LLM tools hand you a blob. Componentization treats an answer as parts—headings, paragraphs, code blocks, steps, or JSON subtrees—with stable IDs and links. You can edit, switch on/off, or regenerate any part, then recompose the final artifact. In early tests, this aligns with how teams actually work: outline first, keep the good bits, surgically fix the bad ones, and reuse components across docs. It’s a small idea with big downstream benefits for control, auditability, and collaboration. ...

Centralized Retrieval-Augmented Generation (RAG) systems promise smarter answers, but they quietly assume one big, clean dataset in one place. Reality is far messier: hospitals, insurers, or financial groups each hold their own silo, often in incompatible formats, and none are willing—or legally allowed—to pool raw data. The HyFedRAG framework tackles this head‑on by making RAG federated, heterogeneous, and privacy‑aware. Edge First, Cloud Second Instead of centralizing records, HyFedRAG runs retrieval at the edge. Each hospital or business unit: ...

TL;DR — L‑MARS replaces single‑pass RAG with a judge‑in‑the‑loop multi‑agent workflow that iteratively searches, checks sufficiency (jurisdiction, date, authority), and only then answers. On a 200‑question LegalSearchQA benchmark of current‑year questions, it reports major gains vs. pure LLMs, at the cost of latency. For regulated industries, the architecture—not just the model—does the heavy lifting. What’s actually new here Most legal QA failures aren’t from weak language skills—they’re from missing or outdated authority. L‑MARS tackles this with three design commitments: ...

Thesis: When the job is to read text, reason carefully, and return a precise number (not just a label), ordinary regression heads and vanilla prompting often fail in opposite ways. The paper introduces MENTAT, a lightweight recipe that marries batch‑reflective prompt evolution with a small MLP aggregator over multiple LLM rollouts. The result: tighter calibration and better ranking on tasks where each example demands real reasoning, not surface features. What counts as “Reasoning‑Intensive Regression” (RiR)? RiR tasks look like this: the model must (1) think through the input with step‑wise analysis, and then (2) score it on a real‑valued scale. The paper frames three such tasks: ...

TL;DR: BusiAgent proposes a client‑centric, multi‑agent LLM framework that formalizes roles (CEO/CFO/CTO/MM/PM) with an extended Continuous‑Time MDP, coordinates them via entropy‑guided brainstorming (peer‑level) and multi‑level Stackelberg games (vertical), and squeezes extra performance from contextual Thompson sampling for prompt optimization—wrapped in a QA stack that fuses STM/LTM memories with a knowledge base. It’s a serious attempt to connect granular analytics to boardroom decisions. The big win is organizational alignment; the big risks are evaluation rigor, token economics, and ops reliability at scale. ...

The gist (and why it matters for business) Enterprise buyers don’t reward demos; they reward repeatable completions per dollar. ComputerRL proposes a path to that by (1) escaping pure GUI mimicry via a machine-first API-GUI action space, (2) scaling online RL across thousands of Ubuntu VMs, and (3) preventing policy entropy collapse with Entropulse—a cadence that alternates RL and supervised fine-tuning (SFT) on successful rollouts. The result: a reported 48.1% OSWorld success with markedly fewer steps than GUI-only agents. Translation for buyers: lower latency, lower cost, higher reliability. ...

TL;DR Today’s long-context and RAG systems scale storage, not thinking. Cognitive Workspace (CW) reframes memory as an active, metacognitive process: curate, plan, reuse, and consolidate. In tests, CW reports ~55–60% memory reuse and 17–18% net efficiency gains despite a 3.3× operation overhead—precisely because it thinks about what to remember and why. The Setup: Context ≠ Cognition Over the past 18 months we’ve cheered >1M-token windows and slicker attention kernels. But piling tokens into a context is like dumping files on a desk; it’s storage without stewardship. In knowledge work, what moves the needle is not how much you can “see” but how well you organize, recall, and reuse—with intent. ...

Most “smart” RAG stacks are actually compulsive googlers: they fetch first and think later. UR² (“Unified RAG and Reasoning”) flips that reflex. It trains a model to reason by default and retrieve only when necessary, using reinforcement learning (RL) to orchestrate the dance between internal knowledge and external evidence. Why this matters for builders: indiscriminate retrieval is the silent cost center of LLM systems—extra latency, bigger bills, brittle answers. UR² shows a way to make retrieval selective, structured, and rewarded, yielding better accuracy on exams (MMLU‑Pro, MedQA), real‑world QA (HotpotQA, Bamboogle, MuSiQue), and even math. ...

In the age of natural language interfaces to databases (NLIDBs), asking the right question has never been easier—or more perilous. While systems like ChatGPT or SQL-Palm can convert everyday English into valid SQL, they often do so without interrogating the quality of the question itself. And as Peter Drucker warned, “The most dangerous thing is asking the wrong question.” Enter VeriMinder, a system built not to improve SQL syntax or execution accuracy—but to diagnose and refine the analytical intent behind the user’s query. It tackles a deceptively simple yet far-reaching problem: a well-formed SQL query that answers a poorly formed question can yield confident but misleading insights. This is particularly problematic in enterprise settings where non-technical users rely on LLM-based BI assistants. ...

Retrieval-Augmented Generation (RAG) systems are fast becoming the connective tissue between Large Language Models (LLMs) and real-world business data. But while RAG systems excel at fetching relevant passages from documents, they often stumble when the data isn’t narrative but numerical. In enterprise environments, where structured formats like HR tables, policy records, or financial reports dominate, this mismatch has become a bottleneck. The paper “Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data” by Chandana Cheerla proposes a much-needed upgrade: a RAG system that treats structured and tabular data as first-class citizens. It doesn’t just flatten tables into linear strings or hope LLMs can reason through semi-garbled inputs. It restructures the entire RAG pipeline to respect and preserve the meaning of tables, rows, and metadata. ...