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# Decoding Consumer Preferences Using Attention-Based Language Models

- Authors: Joshua Foster, Fredrik Odegaard
- Summary: This paper proposes a new method for demand estimation using attention-based language models, specifically an encoder-only language model trained in two stages. It analyzes natural language descriptions of used cars for market demand primitives. The model projects language encodings into the parameter space of a structural model and validates its counterfactual analysis capability on unique zero-shot auction instances.
- Category: econ.EM
- Published: 2025-07-23
- URL: http://arxiv.org/pdf/2507.17564v1

# DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs

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# Natural Language Processing (NLP)

This gist summarizes recent research papers related to Natural Language Processing (NLP) from arXiv.

## Summary of Work

1. **Decoding Consumer Preferences Using Attention-Based Language Models**
   - Proposes a demand estimation method using attention-based language models for analyzing natural language descriptions of used cars to estimate market demand.

2. **DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs**

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# Recent Research Papers on Machine Learning

## Summary of Work

1. **Hierarchical Rectified Flow Matching with Mini-Batch Couplings**: Introduces a hierarchical flow matching model to better capture multi-modality in velocity fields for generative modeling, with benefits shown in synthetic and imaging data.

2. **VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning**: Proposes VisionThink, a dynamic approach to visual token compression in vision-language models using reinforcement learning to optimize token use based on task complexity.

3. **Latent Policy Steering with Embodiment-Agnostic Pretrained World Models**: Focuses on reducing data collection for visuomotor policies by leveraging multi-embodiment datasets using optic flow and World Models, improving policy performance.

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ArXiv Research Summary: quantum computing

Generated on: 2025-07-16 12:43:52

Search Query

quantum computing

Papers Analyzed

Found 5 relevant papers:

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from mirascope import llm, Messages
from mirascope.mcp import sse_client
import lilypad
from pydantic import BaseModel, Field, ValidationError
from tenacity import retry, stop_after_attempt
from fastmcp import FastMCP
from dotenv import load_dotenv
import asyncio

load_dotenv()

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# /// script
# requires-python = ">=3.11"
# dependencies = [
#     "humanlayer==0.7.9",
#     "pydantic==2.11.5",
# ]
# ///

import humanlayer
from datetime import date

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"""Script for generating queries for a recipe search engine.

This script can be used to generate synthetic queries for a recipe search engine.
Following best practices from Hamel and Shreya's AI Evals course, we:

- Generate a set of dimensions that can be used to generate queries; these are attributes that significantly change what the query is about or how it is written.
- For each set of attributes ("Dimensions") we generate a query that matches those attributes.

To ensure that the synthetic generation is better aligned, we first try handwriting the queries using the --manual flag.
This gives us labeled examples to use few shot in our synthetic generation.

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# Agents can fail in fantastic ways, and stacktraces are unfortunately not always helpful enough.
# Fortunately, Pydantic AI agents keep track of messages, so as long as you save them you can view them and aid your debugging!
# This gist shows a simple `try_run` implementation which will log out the messages on error.
# You can imagine any number of things to do with the messages: send to logfire, store in a database, write to file, etc
# Pro tip: these form a helpful subset of data input/outputs to help refine your agent! Taking some time to store them
# appropriately for review (and possibly fine tuning / use in prompts / etc in the future) will pay off!!
These are examples where your agent actually failed!
from __future__ import annotations

import asyncio

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# I found it surprising that tools had to return a JSON-like type; but didn't support Pydantic BaseModels!
# Perhaps that's by design. However, this simple decorator allows you to implement tools with structured output
# and slap a simple decorator to convert to JSON which the Pydantic AI tool format accepts.
# This is a minimal example

from __future__ import annotations

import asyncio
from collections.abc import Coroutine
from dataclasses import dataclass

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# This is a modification of the Pydantic AI weather example from:
# https://ai.pydantic.dev/examples/weather-agent/
# It has been modified to add rate limiting, since the required APIs have tiny rate limits!
# I am using asynciolimiter: uv add asynciolimiter with a custom decorator to apply the rate limits
# Let me know if you know of a better/cleaner way!
# This means we can easily rate limit tools!


from __future__ import annotations as _annotations