We present the Auto-Judge resource for the meta-evaluation of automated LLM judges, especially judges that evaluate Retrieval-Augmented Generation (RAG) systems that ground their response with citations. The resource couples (i) a data release of topics, pooled RAG responses, and human judgments, with (ii) a standardized protocol and software infrastructure for implementing "LLM-as-a-judge" methods in a reproducible and extensible way, including support for parameter sweeps and variant tracking.

The lack of efficiency in neural information retrieval remains one of the primary obstacles to deploying neural retrieval models as a first-stage retriever at scale. While recent tools have improved the standardized measurement of model efficiency, substantial progress is still needed to enable systematic comparative evaluation, for example, in terms of standards for systems and hardware configurations, cloud-based evaluation, benchmarks, and reproducibility. Beyond measurement, the IR community also needs stronger incentives to move in this direction, such as cost-efficiency as a review criterion or as efficiency and/or effectiveness measures in shared tasks, related teaching materials, efficiency-oriented user studies, and specialized awards for efficiency achievements. In particular, developing more efficient variants of highly effective retrieval algorithms should become an admissible research goal for PhD students if cost-efficiency is to become a first-class design objective in~IR. With ReNeuIR, we have established a recurring forum where these questions and new ideas are discussed and where the community comes together to collaboratively evaluate and improve efficiency benchmarking frameworks---most notably through the organization of a shared task focused on efficiency and reproducibility.

Measuring effectiveness and efficiency in information retrieval has a strong empirical background. While modern retrieval systems substantially improve effectiveness, the community has not yet agreed on how to measure efficiency, making it difficult to contrast effectiveness and efficiency fairly. Efficiency-oriented system comparisons are difficult due to factors such as hardware configurations, software versioning, and experimental settings. Efficiency affects users, researchers, and the environment and can be measured in many dimensions beyond time and space, such as resource consumption, water usage, and sample efficiency. Analyzing the efficiency of algorithms and their trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts to designing new measures and guidelines to assess the findings' significance. ReNeuIR's fourth iteration aims to bring the community together to debate these questions and collaboratively test and improve benchmarking frameworks for efficiency based on discussions and collaborations of its previous iterations, including a shared task focused on efficiency and reproducibility.

How good are humans at writing and judging responses in retrieval-augmented generation (RAG) scenarios? To answer this question, we investigate the efficacy of crowdsourcing for RAG through two complementary studies: response writing and response utility judgment. Our new Webis Crowd RAG Corpus 2025 (Webis-CrowdRAG-25) consists of 903 human-written and 903 LLM-generated responses for the 301 topics of the TREC 2024 RAG~track, with each response composed according to one of the three discourse styles 'bullet list', 'essay', or 'news'. For a selection of 65 topics, the corpus further contains 47,320 pairwise human judgments and 10,556 pairwise LLM judgments across seven utility dimensions (e.g., coverage and coherence). Our analyses give insights into human writing behavior for RAG and the viability of crowdsourcing for RAG evaluation. We find that human pairwise judgments provide reliable and cost-effective results. This is much less the case for LLM-based pairwise and human/LLM-based pointwise judgments, nor for automated comparisons with human-written reference responses. All our data and tools are freely available.

The reproducibility and transparency of retrieval experiments depends on the availability of information about the experimental setup. However, the manual collection of experiment metadata can be tedious, error-prone, and inconsistent, which calls for an automated systematic collection. Expanding ir_metadata, we present the TIREx tracker, a tool that records hardware configurations, power/CPU/RAM/GPU usage, and experiment/system versions. Implemented as a lightweight platform-independent C binary, the TIREx tracker integrates seamlessly into Python, Java, or C/C++ workflows and can be easily integrated into shard task submissions, as we demonstrate for the TIRA/TIREx platform. Code, binaries, and documentation of the TIREx tracker are publicly available at https://github.com/tira-io/tirex-tracker.

Transformer-based retrieval approaches typically use the contextualized embedding of the first input token as a dense vector representation for queries and documents. The embeddings of all other tokens are also computed but then discarded, wasting resources. In this paper, we propose the Token-Independent Text Encoder (TITE) as a more efficient modification of the backbone encoder model. Using an attention-based pooling technique, TITE iteratively reduces the sequence length of hidden states layer by layer so that the final output is already a single sequence representation vector. Our empirical analyses on the TREC 2019 and 2020 Deep Learning tracks and the BEIR benchmark show that TITE is on par in terms of effectiveness compared to standard bi-encoder retrieval models while being up to 3.3 times faster at encoding queries and documents. Our code is available at: https://github.com/webis-de/SIGIR-25.