Proof pending. Core topic summary fields are still materializing.
Information retrieval is evolving to address challenges in efficiently retrieving relevant information from large datasets. Recent advancements focus on integrating fine-grained relevance signals, improving robustness against noisy queries, and adapting retrieval methods to dynamic contexts. Techniques such as dual-view retrieval pipelines, query reformulation patterns, and learned graph memory are being developed to enhance retrieval effectiveness and efficiency. These innovations are crucial for builders as they enable more accurate and context-aware information retrieval systems, which are essential for applications in various domains, including materials science, web interactions, and complex query handling. By leveraging these advancements, developers can create systems that better meet user needs and improve overall retrieval performance.
Topic-specific paper and score movement from the daily diff ledger.
We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as an evaluation target. First, we implement a Deep ...
Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain unchanged. We show ...
Large language model (LLM) based listwise reranking has emerged as the dominant paradigm for achieving state-of-the-art ranking effectiveness in information retrieval. However, its reliance on feeding...
User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall no...
Retrieving relevant observations from long multi-modal web interaction histories is challenging because relevance depends on the evolving task state, modality (screenshots, HTML text, structured signa...
Document retrieval identifies relevant documents but does not provide fine-grained evidence cues, such as specific relevant spans. A possible solution is to apply an LLM after retrieval; however, this...
Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not well captured by pa...
While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained global optimization pr...
Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. However, temporal changes in technical corpora, such as API deprecations and co...
We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits short reformulation...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID information-retrieval | Route /topic/information-retrieval
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/information-retrievalMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Information Retrieval",
"cluster": "Information Retrieval"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Information Retrieval",
"normalized_query": "information-retrieval",
"route": "/topic/information-retrieval",
"paper_ref": null,
"topic_slug": "information-retrieval",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.