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新论文 · v1arXiv:2611.12998

Linear Probes to Bayesian Manifolds: Geometry of Arithmetic in Large Language Models

Anshul Kumar, Neha Prasad, Manoj Kumar, Barnabás Póczos, Deeksha Varshney

原文摘要

Linear probes are a standard method to study geometric structures, such as helices and circles, from the activations of large language models (LLMs), and such structure is often taken as evidence that the model computes with it. Probe accuracy alone cannot establish this, because it conflates four separate claims. These are (1) that the concept is decodable from the activations, (2) that they form a specific shape, (3) that the shape belongs to the probed concept rather than to a related concept the model also represents, and (4) that the output of the model depends on the shape. We call these claims decodability, shape, ownership, and causal use, and we test them separately with a four-stage Bayesian pipeline. Stage 2 compares candidate shapes and selects the best one only when its evidence passes a predefined threshold. Stage 3 removes a set of related concepts, specified in advance, and tests whether the shape is owned or inherited. Stage 4 intervenes on the shape to test whether the output of the model depends on it. We apply the pipeline to two-digit addition and multiplication in GPT-J 6B, Llama 3.1 8B, and Pythia 6.9B. Nearly every concept is decodable, but the decoded directions overlap substantially across concepts. Every strongly supported comparison selects a ribbon, which is a helix whose radius changes with the value, or a torus. Of 1,330 cells with an ownership verdict, 19% of shapes are owned and 81% are inherited. Owned and inherited shapes move the output about equally often. Intervening on the shape retains approximately 61% of the effect of intervening on the full probe while using at most five dimensions, against a median of nine for the probe. Our results indicate that a probe verdict alone overstates what a model computes with, and we propose that probe results be reported as verdicts on all four claims rather than as a single accuracy.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07633

Redacting Personal Data: A Speed and Accuracy Comparison of Regex, Presidio and Hybrid Designs

Sai Yashwant, Manoj Kumar Gondimalla, Arjun Sharma, Shubham

原文摘要

Many enterprise projects now pass free text to large language models (LLMs), and that text often contains personally identifiable information (PII) such as names, phone numbers, email addresses, home addresses and dates of birth. Once a prompt is sent, this data is copied into provider systems, application logs and observability traces. Redacting PII before the call is an obvious control, yet teams seldom have numbers on what it costs in latency or what it lets through. This paper compares four redaction designs that any LLM pipeline can adopt: a pure regular-expression (regex) redactor, Microsoft Presidio running on a spaCy model, a hybrid in which Presidio finds only person names while regex handles structured identifiers, and a fast hybrid that calls spaCy's named-entity component directly. All four share a known-entity rule, which removes the person the system already knows it is handling, and a consistent placeholder scheme, so the LLM can still tell one person from another after redaction. We evaluate the designs on a sample workflow, which we call ProfileAssist, that turns resumes into structured profiles, using 100 synthetic resumes (523 characters on average, with 900 PII values and 600 business terms that must survive). Regex ran at a 0.20 ms median but caught only 88.9% of PII, because it cannot find names that are not known in advance, such as the references listed in a resume. Presidio, hybrid and fast hybrid each caught 100% of PII and kept 100% of business terms, at median latencies of 37.59 ms, 30.23 ms and 13.48 ms. Measured against a 9,120 ms median LLM call, even the slowest option adds only 0.41% to the request. We therefore recommend the fast hybrid as a default for LLM pipelines that handle personal data, with regex kept as a safety net rather than as the only control.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01694

Direct Covariance-to-Beamformer Mapping for Low-Latency Hybrid ISAC Beamforming with Subsequent DoA Estimation

Anjali K. S., Manoj Kumar Panda, Chandramani Singh

原文摘要

Integrated sensing and communication (ISAC) systems typically employ a two-stage beamforming pipeline, in which target directions are first estimated from the received covariance matrix and then used to compute the beamforming weights by solving non-convex optimization problems. Although effective, this approach introduces processing latency that can limit beam adaptation in dynamic environments. This letter proposes a beamforming deep neural network (BFDNN) for direct covariance-to-beamformer learning in ISAC systems. BFDNN is a compact regression network that maps covariance-matrix features directly to the hybrid beamforming weights produced by a hybrid ISAC beamformer, eliminating the direction-of-arrival (DoA) estimation stage during inference. Compared with MUSIC, classification-DNN, and regression-DNN based beamforming pipelines, BFDNN generates comparable beam patterns and higher effective spectral efficiencies while reducing the beamforming prediction latency by 25-30 times on the evaluated platform, demonstrating the potential of direct covariance-to-beamformer learning for low-latency ISAC beam updates. Moreover, the target directions can be inferred indirectly from the generated radiation pattern, yielding DoA accuracy competitive with that of dedicated learning-based methods in the evaluated scenario.

cs.LG · eess.SY

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arXiv ↗PDF ↗