RAG

The big idea RAG pipelines are only as reliable as their weakest link: generation that confidently asserts things the sources don’t support. HALT‑RAG proposes an unusually pragmatic fix: don’t fine‑tune a big model—ensemble two strong, frozen NLI models, add lightweight lexical features, train a tiny task‑adapted meta‑classifier, and calibrate it so you can abstain when uncertain. The result isn’t just accuracy; it’s a governable safety control you can dial to meet business risk. ...

If your firm is debating whether to trust an LLM on investment memos, this study is a gift: 1,560 questions from official CFA mock exams across Levels I–III, run on three model archetypes—multimodal generalist (GPT‑4o), deep-reasoning specialist (GPT‑o1), and lightweight cost‑saver (o3‑mini)—both zero‑shot and with a domain‑reasoning RAG pipeline. Below is what matters for adoption, not just leaderboard bragging rights. What the paper really shows Reasoning beats modality for finance. The reasoning‑optimized model (GPT‑o1) dominates across levels; the generalist (GPT‑4o) is inconsistent, especially on math‑heavy Level II. RAG helps where context is long and specialized. Gains are largest at Level III (portfolio cases) and in Fixed Income/Portfolio Management, modest at Level I. Retrieval cannot fix arithmetic. Most errors are knowledge gaps, not reading problems. Readability barely moves accuracy; the bottleneck is surfacing the right curriculum facts and applying them. Cost–accuracy has a sweet spot. o3‑mini + targeted RAG is strong enough for high‑volume workflows; o1 should be reserved for regulated, high‑stakes analysis. Executive snapshot CFA Level GPT‑4o (ZS → RAG) GPT‑o1 (ZS → RAG) o3‑mini (ZS → RAG) Takeaway I 78.6% → 79.4% 94.8% → 94.8% 87.6% → 88.3% Foundations already in‑model; RAG adds little II 59.6% → 60.5% 89.3% → 91.4% 79.8% → 84.3% Level II exposes math + integration gaps; RAG helps smaller models most III 64.1% → 68.6% 79.1% → 87.7% 70.9% → 76.4% Case‑heavy; RAG is decisive, especially for o1 ZS = zero‑shot. Accuracies are from the paper’s aggregated results. ...

Retrieval‑augmented generation tends to pick a side: either lean on labeled exemplars (ICL/L‑RAG) that encode task semantics, or on unlabeled corpora (U‑RAG) that provide broad knowledge. HF‑RAG argues we shouldn’t choose. Instead, it proposes a hierarchical fusion: (1) fuse multiple rankers within each source, then (2) fuse across sources by putting scores on a common scale. The result is a simple, training‑free recipe that improves fact verification and, crucially, generalizes better out‑of‑domain. ...

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 Traditional QA treats software as deterministic; LLM apps aren’t. This paper proposes a three‑layer view (System Shell → Prompt Orchestration → LLM Inference) and argues for a collaborative testing strategy: retain classical testing where it still fits, translate assertions into semantic checks, integrate AI‑safety style probes, and extend QA into runtime. The kicker is AICL, a compact agent‑interaction protocol that bakes in observability, context isolation, and deterministic replay. Why this matters for operators and product teams LLM products now look like systems—not prompts. They combine RAG, tools, stateful multi‑turn workflows, and sometimes multi‑agent handoffs. The result is probabilistic behavior plus cross‑layer failure modes. If you keep writing boolean, exact‑match tests, you’ll ship brittle releases and discover regressions in production. The fix isn’t to abandon testing; it’s to move from asserting single outputs to observing semantic behavior distributions. ...

Most “agent” decks promise autonomy; few explain how to make it shippable. A new survey of LLM‑based agentic reasoning frameworks cuts through the noise with a three‑layer taxonomy—single‑agent methods, tool‑based methods, and multi‑agent methods. Below, we translate that map into a practical build/run playbook for teams deploying AI automation in real workflows. TL;DR Single‑agent = shape the model’s thinking loop (roles, task prompts, reflection, iterative refinement). Tool‑based = widen the model’s action space (APIs, plugins/RAG, middleware; plus selection and orchestration patterns: sequential, parallel, iterative). Multi‑agent = scale division of labor (centralized, decentralized, or hierarchical; with cooperation, competition, negotiation). Treat these as orthogonal dials you tune per use‑case; don’t jump to multi‑agent if a reflective single agent with a code‑interpreter suffices. 1) What’s genuinely new (and useful) here Most prior surveys were model‑centric (how to finetune or RLHF your way to better agents). This survey is framework‑centric: it formalizes the reasoning process—context $C$, action space $A = {a_{reason}, a_{tool}, a_{reflect}}$, termination $Q$—and shows where each method plugs into the loop. That formalism matters for operators: it’s the difference between “let’s try AutoGen” and “we know which knob to turn when the agent stalls, loops, or hallucinates.” ...

Legal judgment prediction (LJP) is one of those problems that exposes the difference between looking smart and being useful. Most models memorize patterns; judges demand reasons. Today’s paper introduces GLARE—an agentic framework that forces the model to widen its hypothesis space, learn from real precedent logic, and fetch targeted legal knowledge only when it needs it. The result isn’t just higher accuracy; it’s a more auditable chain of reasoning. TL;DR What it is: GLARE = Gent Legal Agentic Reasoning Engine for LJP. Why it matters: It turns “guess the label” into compare-and-justify—exactly how lawyers reason. How it works: Three modules—Charge Expansion (CEM), Precedents Reasoning Demonstrations (PRD), and Legal Search–Augmented Reasoning (LSAR)—cooperate in a loop. Proof: Gains of +7.7 F1 (charges) and +11.5 F1 (articles) over direct reasoning; +1.5 to +3.1 F1 over strong precedent‑RAG; double‑digit gains on difficult, long‑tail charges. So what: If you’re deploying LLMs into legal ops or compliance, agentic structure > bigger base model. Why “agentic” beats bigger The usual upgrades—bigger models, more RAG, longer context—don’t address the core failure mode in LJP: premature closure on a familiar charge and surface‑level precedent matching. GLARE enforces a discipline: ...

Why this paper matters: Retrieval‑augmented generation (RAG) has been the default answer to “how do we make LLMs factual?” But clinical work is not a single hop to a single document; it’s a workflow—observe, hypothesize, retrieve, cross‑check, and only then decide. Deep‑DxSearch reframes RAG as a sequential policy, trained end‑to‑end with reinforcement learning (RL) so the model learns when to reason internally and when to consult guidelines, match similar patients, or search broader knowledge—before committing to a diagnosis. That design change is the story. ...

If 2024 was the year AI started writing science, 2025 is making it figure out how to publish it. Today’s paper introduces aiXiv, an open‑access platform where AI agents (and humans) submit proposals, review each other’s work, and iterate until a paper meets acceptance criteria. Rather than bolt AI onto the old gears of journals and preprint servers, aiXiv rebuilds the conveyor belt end‑to‑end. Why this matters (and to whom) Research leaders get a way to pressure‑test automated discovery without waiting months for traditional peer review. AI vendors can plug agents into a standardized workflow (through APIs/MCP), capturing telemetry to prove reliability. Publishers face an existential question: if quality control is measurable and agentic, do we still need the old queue? The core idea in one sentence A closed‑loop, multi‑agent review system combines retrieval‑augmented evaluation, structured critique, and re‑submission cycles to raise the floor of AI‑generated proposals/papers and create an auditable trail of improvements. ...