This paper addresses the problem of robot navigation in mixed geometric and semantic 3D environments. Given a hierarchical representation of the environment, the objective is to navigate from a start position to a goal while minimizing the computational cost. We introduce Hierarchical Class-ordered A* (HCOA*), an algorithm that leverages the environmental hierarchy for efficient path-planning in semantic graphs, significantly reducing computational effort. We use a total order over the semantic classes and prove theoretical performance guarantees for the algorithm. We propose two approaches for higher-layer node classification based on the node semantics of the lowest layer: a Graph Neural Network-based method and a Majority-Class method. We evaluate our approach through simulations on a 3D Scene Graph (3DSG), comparing it to the state-of-the-art and assessing its performance against our classification approaches. Results show that HCOA* can find the optimal path while reducing the number of expanded nodes by 25% and achieving a 16% reduction in computational time on the uHumans2 3DSG dataset.

I am Cherish Elijah(how does it sound?) well let me give you a very short intro about who you have here. As I will always say, I am a versatile writer who has written above fifty different works(I…

Semantic control entails steering LM generations towards satisfying subtle non-lexical constraints, e.g., toxicity, sentiment, or politeness, attributes that can be captured by a sequence-level verifier. It can thus be viewed as sampling from the LM distribution conditioned on the target attribute, a computationally intractable problem due to the non-decomposable nature of the verifier. Existing approaches to LM control either only deal with syntactic constraints which cannot capture the aforementioned attributes, or rely on sampling to explore the conditional LM distribution, an ineffective estimator for low-probability events. In this work, we leverage a verifier's gradient information to efficiently reason over all generations that satisfy the target attribute, enabling precise steering of LM generations by reweighing the next-token distribution. Starting from an initial sample, we create a local LM distribution favoring semantically similar sentences. This approximation enables the tractable computation of an expected sentence embedding. We use this expected embedding, informed by the verifier's evaluation at the initial sample, to estimate the probability of satisfying the constraint, which directly informs the update to the next-token distribution. We evaluated the effectiveness of our approach in controlling the toxicity, sentiment, and topic-adherence of LMs yielding generations satisfying the constraint with high probability (>95%) without degrading their quality.

Learn how to combine the best of keyword and semantic search using SPLADE - a powerful technique that delivers more accurate, transparent, and efficient search results. This practical guide shows you how to implement SPLADE in Elasticsearch to dramatically improve your search capabilities.

Learn how to combine the best of keyword and semantic search using SPLADE - a powerful technique that delivers more accurate, transparent, and efficient search results. This practical guide shows you how to implement SPLADE in Elasticsearch to dramatically improve your search capabilities.

In the contest between Mother Nature's messengers and scientific terminology, guess who's winning?

When I open the AI persona configuration, like Summarizer (short version), I see lots of placeholders in the prompt, like: You are an advanced summarization bot that generates concise, coherent summaries of provided text. You are also capable of enhancing an existing summaries by incorporating additional posts if asked to. - Only include the summary, without any additional commentary. - You understand and generate Discourse forum Markdown; including links, _italics_, **bold**. - Maintain the o...

SAHM or working, you need something else going on

Disappearing data.

Semantic caching is poised to eliminate redundant LLM queries and improve AI agent performance.

Introduction

Soon, you might not need to type in the exact name or spelling for searches in File Explorer, Settings, or Windows Search. But like with many AI-powered features, this one still needs work. Here's how to try it yourself.

— “semicolon,” a punctuation mark that is much more powerful than a comma. as it gives a stronger pause and connects two independent clauses when they are closely related in meaning. it might just…