{"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:28Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:28Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": [], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:03:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:03:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": [], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:18:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:18:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": [], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:33:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:33:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:48:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:48:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:33:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:33:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T18:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T18:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:03:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:03:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:33:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:33:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:03:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:03:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:18:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:18:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:33:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:33:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T22:03:36Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T22:03:36Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:03:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:03:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:03:41Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:03:41Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:18:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:18:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:33:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:33:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T03:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T03:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T04:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Structured Personality Control and Adaptation for LLM Agents", "link": "https://arxiv.org/abs/2601.10025", "summary": "arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement, decision-making, and perceived realism. Personality, in particular, is critical, yet existing approaches often struggle to achieve both nuanced and adaptable expression. We present a framework that models LLM personality via Jungian psychological types, integrating three mechani", "tags": [], "hash": "28da4daa36eecfe9"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T04:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SagaScale: A Realistic, Scalable, and High-Quality Long-Context Benchmark Built from Full-Length Novels", "link": "https://arxiv.org/abs/2601.09723", "summary": "arXiv:2601.09723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-context benchmark built from full-length novels. The entire benchmark is constructed using an automated data collection pipeline that utilizes external resources (e.g., Wikipedia pages) to curate ", "tags": ["docs", "templates"], "hash": "a29f2ac40ab2c3b2"}
{"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Emergent, not Immanent: A Baradian Reading of Explainable AI", "link": "https://arxiv.org/abs/2601.15029", "summary": "arXiv:2601.15029v1 Announce Type: new Abstract: Explainable AI (XAI) is frequently positioned as a technical problem of revealing the inner workings of an AI model. This position is affected by unexamined onto-epistemological assumptions: meaning is treated as immanent to the model, the explainer is positioned outside the system, and a causal structure is presumed recoverable through computational techniques. In this paper, we draw on Barad's agential realism to develop an alternative onto-epistemology of XAI. We propose that interpretations are material-discursive performances that emerge fro", "tags": ["docs", "templates"], "hash": "6bbb1bac30169de3"}
{"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:48Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "HD-GEN: A High-Performance Software System for Human Mobility Data Generation Based on Patterns of Life", "link": "https://arxiv.org/abs/2601.01219", "summary": "arXiv:2601.01219v2 Announce Type: replace Abstract: Understanding individual-level human mobility is critical for a wide range of applications. As such, real-world trajectory datasets provide valuable insights into actual movement behaviors and patterns of life but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offers scalability and flexibility but frequently lacks realism. To address this gap, we introduce a comprehensive software pipeline for, generating, calibrating, processing, and visualizing large-scale individual-level human mobility datasets ", "tags": ["docs", "permits", "templates"], "hash": "824b89a93c84549f"}